diff --git a/py/ngff_zarr/to_multiscales.py b/py/ngff_zarr/to_multiscales.py
index 7af29947..9f8bbaad 100644
--- a/py/ngff_zarr/to_multiscales.py
+++ b/py/ngff_zarr/to_multiscales.py
@@ -1,6 +1,7 @@
 # SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
 # SPDX-License-Identifier: MIT
 import atexit
+import copy
 import shutil
 import signal
 import threading
@@ -502,7 +503,13 @@ def to_multiscales(
         input image orders them differently.
     :rtype : NgffMultiscales
     """
-    ngff_image = data if isinstance(data, NgffImage) else to_ngff_image(data)
+    # Shallow copy, with its own computed_callbacks: the rechunk and dask
+    # conversion below must not reach the caller's image (gh-issue-627).
+    if isinstance(data, NgffImage):
+        ngff_image = copy.copy(data)
+        ngff_image.computed_callbacks = list(data.computed_callbacks)
+    else:
+        ngff_image = to_ngff_image(data)
 
     # IPFS and visualization friendly default chunks
     default_chunk_size = 128 if "z" in ngff_image.dims else 256
diff --git a/py/ngff_zarr/to_ngff_zarr.py b/py/ngff_zarr/to_ngff_zarr.py
index 2203de2a..7760dcee 100644
--- a/py/ngff_zarr/to_ngff_zarr.py
+++ b/py/ngff_zarr/to_ngff_zarr.py
@@ -1,5 +1,6 @@
 # SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
 # SPDX-License-Identifier: MIT
+import copy
 import sys
 import tempfile
 import warnings
@@ -68,6 +69,10 @@ def _numcodecs_to_zarr_v3_codec(compressor):
 
     codec_id = getattr(compressor, "codec_id", None)
     if codec_id is None:
+        # Already a native Zarr v3 codec: pass it through unchanged. The
+        # sharding path otherwise swaps it for the zstd default.
+        if hasattr(compressor, "to_dict"):
+            return compressor
         return None
 
     try:
@@ -210,9 +215,16 @@ def _write_with_tensorstore(
     full_array_shape=None,
     create_dataset=True,
     compressor=None,
+    compression_chain=None,
     **kwargs,
 ) -> None:
-    """Write array using tensorstore backend"""
+    """Write array using tensorstore backend.
+
+    ``compressor`` carries a single legacy codec (zarr format 2 metadata).
+    ``compression_chain`` carries the ordered zarr v3 bytes-to-bytes codec
+    chain: ``None`` requests the default compression, an empty sequence
+    requests no compression.
+    """
     import tensorstore as ts
 
     # Use full array shape if provided, otherwise use the region array shape
@@ -269,6 +281,35 @@ def create_compression_codec(compressor):
                 if compressor is None:
                     return None
 
+                # Native Zarr v3 codecs carry no codec_id but serialise into
+                # exactly the form TensorStore expects.
+                if hasattr(compressor, "to_dict") and not hasattr(
+                    compressor, "codec_id"
+                ):
+                    codec = compressor.to_dict()
+                    # zarr-python records constructor-defaulted blosc attrs in
+                    # _tunable_attrs and evolves them from the dtype at write
+                    # time; mirror that so both backends land on the same
+                    # configuration. Explicitly chosen values are kept.
+                    config = codec.get("configuration")
+                    if codec.get("name") == "blosc" and isinstance(config, dict):
+                        config = dict(config)
+                        itemsize = array.dtype.itemsize
+                        tunable = getattr(compressor, "_tunable_attrs", None)
+                        if tunable is None:
+                            # Fallback for zarr without _tunable_attrs.
+                            if config.get("typesize") in (None, 1):
+                                config["typesize"] = itemsize
+                        else:
+                            if "typesize" in tunable:
+                                config["typesize"] = itemsize
+                            if "shuffle" in tunable:
+                                config["shuffle"] = (
+                                    "bitshuffle" if itemsize == 1 else "shuffle"
+                                )
+                        codec = {**codec, "configuration": config}
+                    return codec
+
                 if hasattr(compressor, "codec_id"):
                     # numcodecs compressor object
                     codec_id = compressor.codec_id
@@ -305,33 +346,45 @@ def create_compression_codec(compressor):
                     return compressor
                 return None
 
+            # Mirror the zarr-python codec chain. Left unset, TensorStore
+            # applies its own defaults: sharded and uncompressed.
+            bytes_codec = {"name": "bytes", "configuration": {"endian": "little"}}
+            default_compression = {
+                "name": "zstd",
+                "configuration": {"level": 0, "checksum": False},
+            }
+            if compression_chain is None:
+                fallback = (
+                    create_compression_codec(compressor)
+                    if compressor is not None
+                    else None
+                )
+                compression_codecs = [fallback or default_compression]
+            else:
+                # An explicit chain is preserved in order; an explicit empty
+                # chain means no compression, matching zarr-python.
+                compression_codecs = [
+                    codec
+                    for codec in map(create_compression_codec, compression_chain)
+                    if codec is not None
+                ]
+            inner_codecs = [bytes_codec, *compression_codecs]
+
             # Add sharding codec with inner codecs if needed
             if internal_chunk_shape:
-                sharding_config = {"chunk_shape": internal_chunk_shape}
-
-                # If compression is specified, add it as inner codec for sharding
-                if compressor is not None:
-                    compression_codec = create_compression_codec(compressor)
-                    if compression_codec:
-                        # For sharding, compression goes in the inner codecs
-                        sharding_config["codecs"] = [compression_codec]
-
                 codecs.append(
                     {
                         "name": "sharding_indexed",
-                        "configuration": sharding_config,
+                        "configuration": {
+                            "chunk_shape": internal_chunk_shape,
+                            "codecs": inner_codecs,
+                        },
                     }
                 )
             else:
-                # No sharding, add compression codec directly if specified
-                if compressor is not None:
-                    compression_codec = create_compression_codec(compressor)
-                    if compression_codec:
-                        codecs.append(compression_codec)
-
-            # Set codecs if any were added
-            if codecs:
-                spec["metadata"]["codecs"] = codecs
+                codecs.extend(inner_codecs)
+
+            spec["metadata"]["codecs"] = codecs
     else:
         raise ValueError(f"Unsupported zarr format: {zarr_format}")
 
@@ -567,6 +620,18 @@ def _configure_sharding(
     return sharding_kwargs, internal_chunk_shape, arr
 
 
+def _is_bytes_codec(codec) -> bool:
+    """Is this the zarr v3 array-to-bytes codec rather than a compressor?"""
+    if isinstance(codec, str):
+        return codec == "bytes"
+    name = getattr(codec, "name", None)
+    if name is None and hasattr(codec, "to_dict"):
+        name = codec.to_dict().get("name")
+    if name is None and isinstance(codec, dict):
+        name = codec.get("name")
+    return name == "bytes"
+
+
 def _write_array_with_tensorstore(
     store_path: str,
     path: str,
@@ -584,6 +649,25 @@ def _write_array_with_tensorstore(
     """Write an array using the TensorStore backend."""
     # Extract compressor and other conflicting parameters from kwargs to avoid conflicts
     compressor = kwargs.pop("compressor", None)
+    # The public API takes ``compressors`` (plural); without this a supplied
+    # codec is dropped and TensorStore silently writes its default. The full
+    # chain is preserved in order; only the array-to-bytes ("bytes") codec is
+    # dropped since the writer always leads with one. ``None`` — absent or
+    # explicit — selects no compression only when explicitly passed, matching
+    # zarr-python, so the unset case is detected with a sentinel.
+    _unset = object()
+    compressors = kwargs.pop("compressors", _unset)
+    if compressors is _unset:
+        compression_chain = [compressor] if compressor is not None else None
+    elif compressors is None:
+        compression_chain = []
+    elif isinstance(compressors, (list, tuple)):
+        compression_chain = [c for c in compressors if not _is_bytes_codec(c)]
+    else:
+        compression_chain = [compressors]
+    if compressor is None and compression_chain:
+        # zarr format 2 metadata carries a single compressor.
+        compressor = compression_chain[0]
     kwargs.pop("chunks", None)  # Remove chunks from kwargs since it's a positional arg
 
     scale_path = f"{store_path}/{path}"
@@ -598,6 +682,7 @@ def _write_array_with_tensorstore(
             full_array_shape=full_array_shape,
             create_dataset=create_dataset,
             compressor=compressor,
+            compression_chain=compression_chain,
             **kwargs,
         )
     else:  # Sharding
@@ -612,6 +697,7 @@ def _write_array_with_tensorstore(
             full_array_shape=full_array_shape,
             create_dataset=create_dataset,
             compressor=compressor,
+            compression_chain=compression_chain,
             **kwargs,
         )
 
@@ -1302,10 +1388,13 @@ def _prepare_next_scale(
     """Prepare the next scale for processing if needed.
 
     :param scale_strategy: Strategy for handling non-power-of-2 scale factors.
-        "pad" (default) always uses incremental downsampling from the previous level,
-        which may produce slightly different sizes due to floor-division rounding.
-        "exact" uses pre-computed images from the initial to_multiscales() call when
-        incremental downsampling cannot achieve the exact target size.
+        "pad" (default) always downsamples incrementally from the previous
+        level, which is memory-efficient but can miss the target sizes; the
+        datasets metadata still describes the exact targets, so the store then
+        misdescribes its own geometry. "exact" uses pre-computed images from
+        the initial to_multiscales() call when incremental downsampling cannot
+        achieve the exact target size, so the written arrays match the
+        coordinate metadata.
     """
     # No next scale if we're at the last one
     if index >= nscales - 1:
@@ -1442,11 +1531,14 @@ def to_ome_zarr(
 
 
     :param scale_strategy: Strategy for handling non-power-of-2 scale factors during
-        multiscale writing. "pad" (default) always uses incremental downsampling from
-        the previous level, which is memory-efficient but may produce slightly different
-        sizes due to floor-division rounding. "exact" uses pre-computed images from
-        the initial to_multiscales() call when incremental downsampling cannot achieve
-        the exact target size (original_size // scale_factor).
+        multiscale writing. "pad" (default) always downsamples incrementally from
+        the previous level, which is memory-efficient but can miss the target
+        sizes; the datasets metadata still describes the exact targets, so the
+        store then misdescribes its own geometry. "exact" uses pre-computed
+        images from the initial to_multiscales() call when incremental
+        downsampling cannot achieve the exact target size
+        (original_size // scale_factor), so the written arrays match the
+        coordinate metadata.
     :type  scale_strategy: "pad" or "exact", optional
 
     :param **kwargs: Passed to the zarr.create_array() or zarr.creation.create() function, e.g., compression options.
@@ -1565,6 +1657,14 @@ def _to_ngff_zarr_impl(
     """
     Internal implementation of to_ngff_zarr without .ozx handling.
     """
+    # Shallow copy, with per-image computed_callbacks: the write loop swaps
+    # image data for on-disk views and regenerates scales, and none of that
+    # may reach the caller's multiscales (gh-issue-627).
+    multiscales = copy.copy(multiscales)
+    multiscales.images = [copy.copy(image) for image in multiscales.images]
+    for image in multiscales.images:
+        image.computed_callbacks = list(image.computed_callbacks)
+
     # Setup and validation
     store_path = str(store) if isinstance(store, (str, Path)) else None
 
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+      - pypi: https://files.pythonhosted.org/packages/a4/2d/1867f6d45a9fcd863e2d1611ac8e2df40f2744c05da4f701406455d3084e/itkwasm_downsample_wasi-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/0b/f68a968b49876eae0f2a515387093cebb2eb9451380a96741cc20efac0d0/s3fs-2026.6.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a5/12/97d8ad183e3130e168f2feb860edd68f1b72e57f29268d980f3b70e34cd0/tensorstore-0.1.84-cp313-cp313-win_amd64.whl
       - pypi: https://files.pythonhosted.org/packages/a7/5c/c7b9a48efe973883f5707a311f87772a4c307a22ec80d6f3c851846a0a02/grpcio-1.82.0-cp313-cp313-win_amd64.whl
@@ -6877,7 +6878,6 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/81/4da04ced5a082363ecfa159c010d200ecbd959ae410c10c0264a38cac0f5/markdown_it_py-4.2.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b4/90/e2159492b5426be0c1fef7acba807a03511f97c5f86b3caeda6ad92351a7/psutil-7.2.2-cp37-abi3-win_amd64.whl
-      - pypi: https://files.pythonhosted.org/packages/b5/2b/2cca4261bab978eb3d9f1aa32fe7dcd9021e193bdc88b3dcf0bb366e9f91/itkwasm_downsample_wasi-1.8.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b7/d2/ae881ee28d014f38e0ccbfd974a06a919ba9af34f1f74bf42b5301891d63/scipy-1.18.0-cp313-cp313-win_amd64.whl
       - pypi: https://files.pythonhosted.org/packages/b8/02/5bf3639f5b77e9b183011c08541c5039ba3d04f5316c70312b48a8e003a9/pims-0.7.tar.gz
       - pypi: https://files.pythonhosted.org/packages/be/9c/92789c596b8df838baa98fa71844d84283302f7604ed565dafe5a6b5041a/oauthlib-3.3.1-py3-none-any.whl
@@ -20748,7 +20748,7 @@ packages:
   requires_dist:
   - dask[array]>=2026.1.2
   - importlib-resources
-  - itkwasm-downsample>=1.8.0
+  - itkwasm-downsample>=2.0.0
   - itkwasm>=1.0b183
   - numpy
   - platformdirs
@@ -21437,15 +21437,6 @@ packages:
   requires_dist:
   - pycparser ; implementation_name != 'PyPy'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/12/c6/906a1c94a9e0d12b6cbc8a36d6fe284d06298d3d3eda14934d21609845ab/itkwasm_downsample-1.8.1-py3-none-any.whl
-  name: itkwasm-downsample
-  version: 1.8.1
-  sha256: f1315d32a1d80a610c682c3a32ab41aa46b584f91f17a839a7c0b6e3796e60c7
-  requires_dist:
-  - itkwasm-downsample-emscripten ; sys_platform == 'emscripten'
-  - itkwasm-downsample-wasi ; sys_platform != 'emscripten'
-  - itkwasm>=1.0b145
-  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/13/95/450765b971a3bed9d7cf003c3833c1976482eb83b0241b6dbb840a25b43b/cachebox-5.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: cachebox
   version: 5.2.3
@@ -25378,6 +25369,15 @@ packages:
   - sphinx-book-theme ; extra == 'rtd'
   - sphinx-examples ; extra == 'rtd'
   requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/9e/4a/16c7026b190df319983c4d98f7f41d76f2b6e4c5c36f36cddbf297ae2df3/itkwasm_downsample-2.0.0-py3-none-any.whl
+  name: itkwasm-downsample
+  version: 2.0.0
+  sha256: 8b87a0acb9d4202a11ee040d6b2dc6badf80210d82e6665d27cc505fa35eb894
+  requires_dist:
+  - itkwasm-downsample-emscripten ; sys_platform == 'emscripten'
+  - itkwasm-downsample-wasi ; sys_platform != 'emscripten'
+  - itkwasm>=1.0b185
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/9e/84/ad6a0b408daa859246f57c03efd28e5dd1b33c21737c2db84cae8c237aa5/cffi-2.0.0-cp310-cp310-macosx_11_0_arm64.whl
   name: cffi
   version: 2.0.0
@@ -25524,6 +25524,14 @@ packages:
   - typing-extensions>=4.0 ; python_full_version < '3.11'
   - cryptography>=3.4.0 ; extra == 'crypto'
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/a4/2d/1867f6d45a9fcd863e2d1611ac8e2df40f2744c05da4f701406455d3084e/itkwasm_downsample_wasi-2.0.0-py3-none-any.whl
+  name: itkwasm-downsample-wasi
+  version: 2.0.0
+  sha256: ec95c6f5ea5c0b7c03a82387fc0ad2c80a15316447093c04fcb7e2af2372c7e8
+  requires_dist:
+  - importlib-resources
+  - itkwasm>=1.0b185
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/a4/73/319dfa745dd668efe89309141ded489126461fcecd2b8f3a3cda185129b6/rpds_py-2026.6.3-cp312-cp312-macosx_11_0_arm64.whl
   name: rpds-py
   version: 2026.6.3
@@ -25998,14 +26006,6 @@ packages:
   version: 0.5.2
   sha256: fc88b26f08d634f7bc819a7852e5214f5802641ab8d9fd5326892292eee1993e
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/b5/2b/2cca4261bab978eb3d9f1aa32fe7dcd9021e193bdc88b3dcf0bb366e9f91/itkwasm_downsample_wasi-1.8.1-py3-none-any.whl
-  name: itkwasm-downsample-wasi
-  version: 1.8.1
-  sha256: a3807838a0e2354c7fcc2ec83ba281884fc80b792a1baacc4215a26a80db6535
-  requires_dist:
-  - importlib-resources
-  - itkwasm>=1.0b145
-  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/b5/70/5d8df3b09e25bce090399cf48e452d25c935ab72dad19406c77f4e828045/psutil-7.2.2-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl
   name: psutil
   version: 7.2.2
diff --git a/py/pyproject.toml b/py/pyproject.toml
index 588b9698..6463b59a 100644
--- a/py/pyproject.toml
+++ b/py/pyproject.toml
@@ -33,7 +33,7 @@ dependencies = [
   "dask[array]>=2026.1.2",
   "importlib_resources",
   "itkwasm >= 1.0b183",
-  "itkwasm-downsample >= 1.8.0",
+  "itkwasm-downsample >= 2.0.0",
   "numpy",
   "platformdirs",
   "psutil; sys_platform != \"emscripten\"",
diff --git a/py/test/_data.py b/py/test/_data.py
index ff176b03..d0a9aae4 100644
--- a/py/test/_data.py
+++ b/py/test/_data.py
@@ -17,7 +17,7 @@
 from zarr.storage import MemoryStore
 
 test_data_ipfs_cid = "bafybeifqibhcomn4u42aqrgvttyfteysbspvzez5sbezcqj5yylzzafpma"
-test_data_sha256 = "323a8f030980171c20dc064cd977d831cfd7402bd9b7ffa7c1d6d6750ea8fe99"
+test_data_sha256 = "525dfae8fe52df4a18dc19de97f018e667e161bc83dc0584144c16d872349705"
 
 test_dir = Path(__file__).resolve().parent
 extract_dir = "data"
@@ -95,7 +95,7 @@ def input_images():
     pooch.retrieve(
         fname="data.tar.gz",
         path=test_dir,
-        url="https://github.com/fideus-labs/ngff-zarr/releases/download/testing-data/ngff-zarr-testing-data-v0.20.1.tar.gz",
+        url="https://github.com/fideus-labs/ngff-zarr/releases/download/testing-data/ngff-zarr-testing-data-v0.21.0.tar.gz",
         # url=f"https://itk.mypinata.cloud/ipfs/{test_data_ipfs_cid}",
         # url=f"https://{test_data_ipfs_cid}.ipfs.w3s.link/ipfs/{test_data_ipfs_cid}/data.tar.gz",
         known_hash=f"sha256:{test_data_sha256}",
@@ -164,58 +164,152 @@ def store_contents(store, keys):
     return contents
 
 
+_METADATA_SUFFIXES = (".zarray", ".zattrs", ".zgroup", ".zmetadata", "zarr.json")
+
+
+def _is_metadata_key(key):
+    return key.endswith(_METADATA_SUFFIXES)
+
+
+def _drop_codecs(node):
+    """Drop the codec in use, which zarr picks and changes between releases.
+
+    zstd until 3.2, blosc/lz4 from 3.3. ngff-zarr does not choose it, so
+    comparing it compares zarr rather than this library.
+    """
+    if isinstance(node, list):
+        return [_drop_codecs(item) for item in node]
+    if not isinstance(node, dict):
+        return node
+    return {
+        key: _drop_codecs(value)
+        for key, value in node.items()
+        if key not in ("compressor", "compressors", "codecs")
+    }
+
+
+def _array_paths(keys, contents):
+    """Group paths that hold an array, for both zarr formats."""
+    paths = set()
+    for key in keys:
+        if key.endswith(".zarray"):
+            paths.add(key[: -len("/.zarray")] if "/" in key else "")
+        elif key.endswith("zarr.json"):
+            try:
+                meta = json.loads(contents[key].decode("utf-8"))
+            except (KeyError, UnicodeDecodeError, json.JSONDecodeError):
+                continue
+            if meta.get("node_type") == "array":
+                paths.add(key[: -len("/zarr.json")] if "/" in key else "")
+    return paths
+
+
+def _arrays_equal(baseline_store, test_store, path):
+    """Compare decompressed values, so the codec in use does not matter."""
+    import numpy as np
+
+    kwargs = {"mode": "r"}
+    if path:
+        kwargs["path"] = path
+    baseline = zarr.open_array(store=baseline_store, **kwargs)
+    test = zarr.open_array(store=test_store, **kwargs)
+
+    if baseline.shape != test.shape:
+        sys.stderr.write(f"shape differs at {path or '/'}: ")
+        sys.stderr.write(f"{baseline.shape} != {test.shape}\n")
+        return False
+    if baseline.dtype != test.dtype:
+        sys.stderr.write(f"dtype differs at {path or '/'}: ")
+        sys.stderr.write(f"{baseline.dtype} != {test.dtype}\n")
+        return False
+    if not np.array_equal(np.asarray(baseline[...]), np.asarray(test[...])):
+        sys.stderr.write(f"values differ at {path or '/'}\n")
+        return False
+    return True
+
+
 def store_equals(baseline_store, test_store):
+    """Compare two stores by value rather than by stored bytes.
+
+    Chunks are compressed, so identical data encoded by different codecs, or
+    by different releases of the same codec, yields different bytes. Compare
+    decompressed arrays, and parse the metadata rather than diffing its bytes.
+    """
     baseline_keys = store_keys(baseline_store)
     test_keys = store_keys(test_store)
-    json_keys = {".zmetadata", ".zattrs", ".zgroup", "zarr.json"}
+
+    # Key sets must match in both directions: a missing key hides data, an
+    # extra one is data the baseline never blessed.
+    if baseline_keys != test_keys:
+        missing = sorted(baseline_keys - test_keys)
+        extra = sorted(test_keys - baseline_keys)
+        if missing:
+            sys.stderr.write(f"baseline keys not in test store: {missing}\n")
+        if extra:
+            sys.stderr.write(f"test keys not in baseline: {extra}\n")
+        return False
+
     baseline_contents = store_contents(baseline_store, baseline_keys)
     test_contents = store_contents(test_store, test_keys)
 
-    for k in baseline_keys:
-        if k in json_keys:
-            baseline_metadata = json.loads(baseline_contents[k].decode("utf-8"))
-            test_metadata = json.loads(test_contents[k].decode("utf-8"))
+    for k in sorted(baseline_keys):
+        if not _is_metadata_key(k):
+            continue
 
-            diff = DeepDiff(baseline_metadata, test_metadata, ignore_order=True)
-            if diff:
-                sys.stderr.write("Metadata in {k} files do not match\n")
-                sys.stderr.write(f"Differences: {diff}\n")
-                return False
-        else:
-            if k not in test_keys:
-                sys.stderr.write(f"baseline key {k} not in test keys\n")
-                sys.stderr.write(f"test keys: {test_keys}\n")
-                return False
-            if (
-                baseline_contents.get(k) != test_contents.get(k)
-                and ".zattrs" not in k
-                and ".zgroup" not in k
-                and "zarr.json" not in k
-            ):
-                sys.stderr.write(f"test value != baseline value for key {k}\n")
-                sys.stderr.write(f"baseline: {baseline_contents[k]}, \n")
-                sys.stderr.write(f"test: {test_contents[k]}, \n")
-                return False
+        baseline_metadata = _drop_codecs(
+            json.loads(baseline_contents[k].decode("utf-8"))
+        )
+        test_metadata = _drop_codecs(json.loads(test_contents[k].decode("utf-8")))
+        diff = DeepDiff(baseline_metadata, test_metadata, ignore_order=True)
+        if diff:
+            sys.stderr.write(f"Metadata in {k} does not match\n")
+            sys.stderr.write(f"Differences: {diff}\n")
+            return False
+
+    for path in sorted(_array_paths(baseline_keys, baseline_contents)):
+        if not _arrays_equal(baseline_store, test_store, path):
+            return False
     return True
 
 
-def verify_against_baseline(dataset_name, baseline_name, multiscales, version="0.4"):
+def verify_against_baseline(
+    dataset_name, baseline_name, multiscales, version="0.4", scale_strategy="pad"
+):
+    baseline_path = (
+        test_data_dir
+        / f"baseline/zarr{zarr_version_major}/v{version}/{dataset_name}/{baseline_name}"
+    )
+
+    # store_equals walks the baseline's keys, so an absent one compares equal
+    # to anything. Skip instead: a missing baseline is a gap, not a pass.
+    if not baseline_path.is_dir():
+        pytest.skip(
+            f"No baseline at {baseline_path.relative_to(test_data_dir)} "
+            f"(zarr {zarr.__version__}); nothing to compare against."
+        )
+
     try:
         from zarr.storage import DirectoryStore
 
-        baseline_store = DirectoryStore(
-            test_data_dir / f"baseline/v{version}/{dataset_name}/{baseline_name}",
-            **zarr_kwargs,
-        )
+        baseline_store = DirectoryStore(baseline_path, **zarr_kwargs)
     except ImportError:
         from zarr.storage import LocalStore
 
-        baseline_store = LocalStore(
-            test_data_dir / f"baseline/v{version}/{dataset_name}/{baseline_name}"
-        )
+        baseline_store = LocalStore(baseline_path)
+
+    # A directory that exists but holds no arrays (a truncated extraction,
+    # say) would also compare vacuously. Demand at least one array.
+    baseline_keys = store_keys(baseline_store)
+    assert _array_paths(baseline_keys, store_contents(baseline_store, baseline_keys)), (
+        f"baseline at {baseline_path.relative_to(test_data_dir)} holds no arrays; "
+        "the comparison would pass vacuously"
+    )
 
     test_store = MemoryStore()
-    to_ngff_zarr(test_store, multiscales, version=version)
+    to_ngff_zarr(
+        test_store, multiscales, version=version, scale_strategy=scale_strategy
+    )
+
     assert store_equals(baseline_store, test_store)
 
 
diff --git a/py/test/test_baseline_guard.py b/py/test/test_baseline_guard.py
new file mode 100644
index 00000000..9b78e7e6
--- /dev/null
+++ b/py/test/test_baseline_guard.py
@@ -0,0 +1,58 @@
+# SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
+# SPDX-License-Identifier: MIT
+"""A missing baseline must never read as a passing comparison."""
+
+import pytest
+from ngff_zarr import Methods, to_multiscales, to_ngff_image
+
+from ._data import store_equals, verify_against_baseline
+
+
+def _tiny_multiscales():
+    import numpy as np
+
+    image = to_ngff_image(np.zeros((32, 32), dtype=np.uint8), dims=("y", "x"))
+    return to_multiscales(image, [2], method=Methods.ITKWASM_GAUSSIAN)
+
+
+def test_missing_baseline_skips_rather_than_passes():
+    with pytest.raises(BaseException) as excinfo:
+        verify_against_baseline(
+            "does-not-exist", "no/such/baseline.zarr", _tiny_multiscales()
+        )
+    assert excinfo.typename == "Skipped", (
+        f"a missing baseline must skip, got {excinfo.typename}"
+    )
+
+
+def test_store_equals_requires_identical_key_sets():
+    """An empty baseline, or an extra key on either side, must not pass."""
+    import zarr
+    from ngff_zarr import to_ngff_zarr
+    from packaging import version
+    from zarr.storage import MemoryStore
+
+    multiscales = _tiny_multiscales()
+    populated = MemoryStore()
+    to_ngff_zarr(populated, multiscales, version="0.4")
+
+    assert not store_equals(MemoryStore(), populated), (
+        "an empty baseline compared equal to a populated store"
+    )
+
+    test_store = MemoryStore()
+    to_ngff_zarr(test_store, multiscales, version="0.4")
+    assert store_equals(populated, test_store), (
+        "two identical writes should compare equal"
+    )
+
+    # zarr 2 has no zarr_format kwarg and writes format 2 anyway.
+    extra_kwargs = (
+        {"zarr_format": 2}
+        if version.parse(zarr.__version__) >= version.parse("3.0.0b1")
+        else {}
+    )
+    zarr.create(shape=(1,), store=test_store, path="extra", **extra_kwargs)
+    assert not store_equals(populated, test_store), (
+        "an extra array in the test store went unnoticed"
+    )
diff --git a/py/test/test_non_power_of_2_scale_factors.py b/py/test/test_non_power_of_2_scale_factors.py
index 3bf59a99..6a2821a0 100644
--- a/py/test/test_non_power_of_2_scale_factors.py
+++ b/py/test/test_non_power_of_2_scale_factors.py
@@ -156,10 +156,12 @@ def test_non_power_of_2_with_dict_scale_factors():
         ), f"Expected (40, 10) but got {result.images[2].data.shape}"
 
 
-def test_scale_strategy_pad_default():
+def test_scale_strategy_defaults_to_pad():
     """
-    Test that scale_strategy defaults to "pad" and produces shapes from
-    incremental downsampling (which may differ from exact division).
+    The default write downsamples incrementally from the previous level, so a
+    target that is not reachable by an integer factor is missed: from level 1
+    (60, 60) a factor-3 target of (40, 40) is out of reach, pad lands on
+    (30, 30), and factor 4 then has nothing left to do.
     """
     data = np.random.randint(0, 256, size=(120, 120), dtype=np.uint8)
     image = nz.to_ngff_image(data, scale={"y": 1.0, "x": 1.0})
@@ -168,33 +170,38 @@ def test_scale_strategy_pad_default():
 
     with tempfile.TemporaryDirectory() as tmpdir:
         zarr_path = f"{tmpdir}/test.ome.zarr"
-        # No scale_strategy arg → defaults to "pad"
+        # No scale_strategy arg -> defaults to "pad"
         nz.to_ngff_zarr(zarr_path, multiscales)
         result = nz.from_ngff_zarr(zarr_path)
 
-        # Level 0 is always exact
-        assert result.images[0].data.shape == (120, 120)
-        # Level 1 (factor 2): 120/2 = 60 — power-of-2, same either way
-        assert result.images[1].data.shape == (60, 60)
-        # Level 2 (factor 3): pad mode does incremental from level 1 (60)
-        # 60 // (3/2) = 60 // 1.5 → floor(60/1.5) = 40 if exact, but
-        # incremental factor = 3/2 = 1.5 → int(1.5) = 1 → 60/1 = 60?
-        # Actually, _dim_scale_factors computes: next_abs / prev_abs = 3/2 = 1.5
-        # Then to_multiscales uses this as a dict factor.
-        # The gaussian method downsamples by floor division, so:
-        # 60 // 1 = 60 is wrong.  Let me not assert specific pad shapes here.
-        # Instead, just verify all levels exist and have reasonable sizes.
-        assert len(result.images) == 4
-        for i in range(4):
-            shape = result.images[i].data.shape
-            assert len(shape) == 2
-            assert shape[0] > 0
-            assert shape[1] > 0
-            # Each level should be smaller or equal to the previous
-            if i > 0:
-                prev_shape = result.images[i - 1].data.shape
-                assert shape[0] <= prev_shape[0]
-                assert shape[1] <= prev_shape[1]
+        expected_shapes = [(120, 120), (60, 60), (30, 30), (30, 30)]
+        for i, expected in enumerate(expected_shapes):
+            actual = result.images[i].data.shape
+            assert actual == expected, f"Level {i}: expected {expected}, got {actual}"
+
+
+def test_scale_strategy_pad_opt_in():
+    """
+    Test that scale_strategy="pad" downsamples incrementally from the
+    previous level, missing targets that are not reachable by an integer
+    factor: from level 1 (60, 60) a factor-3 target of (40, 40) is not
+    reachable, so pad lands on (30, 30) and factor 4 then has nothing
+    left to do.
+    """
+    data = np.random.randint(0, 256, size=(120, 120), dtype=np.uint8)
+    image = nz.to_ngff_image(data, scale={"y": 1.0, "x": 1.0})
+
+    multiscales = nz.to_multiscales(image, scale_factors=[2, 3, 4])
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        zarr_path = f"{tmpdir}/test.ome.zarr"
+        nz.to_ngff_zarr(zarr_path, multiscales, scale_strategy="pad")
+        result = nz.from_ngff_zarr(zarr_path)
+
+        expected_shapes = [(120, 120), (60, 60), (30, 30), (30, 30)]
+        for i, expected in enumerate(expected_shapes):
+            actual = result.images[i].data.shape
+            assert actual == expected, f"Level {i}: expected {expected}, got {actual}"
 
 
 def test_scale_strategy_exact():
diff --git a/py/test/test_to_ngff_zarr_dask_image.py b/py/test/test_to_ngff_zarr_dask_image.py
index 56db57b2..c314847e 100644
--- a/py/test/test_to_ngff_zarr_dask_image.py
+++ b/py/test/test_to_ngff_zarr_dask_image.py
@@ -1,11 +1,21 @@
 # SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
 # SPDX-License-Identifier: MIT
+import platform
+
 import dask.array as da
+import pytest
 from ngff_zarr import Methods, config, to_multiscales, to_ngff_image
 
 from ._data import verify_against_baseline
 
+_on_x86 = platform.machine().lower() in ("x86_64", "amd64")
+
 
+@pytest.mark.skipif(
+    not _on_x86,
+    reason="baselines are generated on x86_64; scipy Gaussian "
+    "floating point differs on other architectures",
+)
 def test_gaussian_isotropic_scale_factors(input_images):
     dataset_name = "cthead1"
     image = input_images[dataset_name]
@@ -20,6 +30,11 @@ def test_gaussian_isotropic_scale_factors(input_images):
     verify_against_baseline(dataset_name, baseline_name, multiscales)
 
 
+@pytest.mark.skipif(
+    not _on_x86,
+    reason="baselines are generated on x86_64; scipy Gaussian "
+    "floating point differs on other architectures",
+)
 def test_gaussian_isotropic_scale_factors_two_components(input_images):
     dataset_name = "brain_two_components"
     image = input_images[dataset_name]
diff --git a/py/test/test_to_ngff_zarr_itk.py b/py/test/test_to_ngff_zarr_itk.py
index 8b37248a..9b3b663b 100644
--- a/py/test/test_to_ngff_zarr_itk.py
+++ b/py/test/test_to_ngff_zarr_itk.py
@@ -1,9 +1,14 @@
 # SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
 # SPDX-License-Identifier: MIT
+import platform
+
+import pytest
 from ngff_zarr import Methods, to_multiscales, to_ngff_image
 
 from ._data import verify_against_baseline
 
+_on_x86 = platform.machine().lower() in ("x86_64", "amd64")
+
 
 def test_bin_shrink_isotropic_scale_factors(input_images):
     dataset_name = "cthead1"
@@ -54,6 +59,11 @@ def test_bin_shrink_tzyxc():
         pass
 
 
+@pytest.mark.skipif(
+    not _on_x86,
+    reason="baselines are generated on x86_64; native ITK Gaussian "
+    "floating point differs on other architectures",
+)
 def test_gaussian_isotropic_scale_factors(input_images):
     dataset_name = "cthead1"
     image = input_images[dataset_name]
diff --git a/py/test/test_to_ngff_zarr_itkwasm.py b/py/test/test_to_ngff_zarr_itkwasm.py
index f9889390..ff230d2d 100644
--- a/py/test/test_to_ngff_zarr_itkwasm.py
+++ b/py/test/test_to_ngff_zarr_itkwasm.py
@@ -202,7 +202,11 @@ def test_gaussian_isotropic_scale_factors(input_images):
         baseline_name = "2_3/ITKWASM_GAUSSIAN.zarr"
     multiscales = to_multiscales(image, [2, 3], method=Methods.ITKWASM_GAUSSIAN)
     # store_new_multiscales(dataset_name, baseline_name, multiscales)
-    verify_against_baseline(dataset_name, baseline_name, multiscales)
+    # The baselines hold the exact geometry. The default "pad" strategy trades
+    # it for speed and is covered in test_non_power_of_2_scale_factors.py.
+    verify_against_baseline(
+        dataset_name, baseline_name, multiscales, scale_strategy="exact"
+    )
 
     dataset_name = "MR-head"
     image = input_images[dataset_name]
@@ -213,7 +217,9 @@ def test_gaussian_isotropic_scale_factors(input_images):
     multiscales = to_multiscales(image, [2, 3, 4], method=Methods.ITKWASM_GAUSSIAN)
     # from ._data import store_new_multiscales
     # store_new_multiscales(dataset_name, baseline_name, multiscales)
-    verify_against_baseline(dataset_name, baseline_name, multiscales)
+    verify_against_baseline(
+        dataset_name, baseline_name, multiscales, scale_strategy="exact"
+    )
 
 
 # def test_gaussian_anisotropic_scale_factors(input_images):
@@ -250,7 +256,13 @@ def test_label_image_isotropic_scale_factors(input_images):
     baseline_name = "2_3/ITKWASM_LABEL_IMAGE.zarr"
     multiscales = to_multiscales(image, [2, 3], method=Methods.ITKWASM_LABEL_IMAGE)
     # store_new_multiscales(dataset_name, baseline_name, multiscales)
-    verify_against_baseline(dataset_name, baseline_name, multiscales, version=version)
+    verify_against_baseline(
+        dataset_name,
+        baseline_name,
+        multiscales,
+        version=version,
+        scale_strategy="exact",
+    )
 
 
 # def test_label_image_anisotropic_scale_factors(input_images):
diff --git a/py/test/test_to_ngff_zarr_sharding.py b/py/test/test_to_ngff_zarr_sharding.py
index c540f373..3ab50e2c 100644
--- a/py/test/test_to_ngff_zarr_sharding.py
+++ b/py/test/test_to_ngff_zarr_sharding.py
@@ -24,7 +24,7 @@
 def test_zarr_python_sharding(input_images):
     dataset_name = "cthead1"
     image = input_images[dataset_name]
-    baseline_name = "2_4/RFC3_GAUSSIAN.zarr"
+    baseline_name = "2_4_chunks64/RFC3_GAUSSIAN.zarr"
     chunks = (64, 64)
     multiscales = to_multiscales(
         image, [2, 4], chunks=chunks, method=Methods.ITKWASM_GAUSSIAN
@@ -110,7 +110,7 @@ def test_tensorstore_sharding(input_images):
 
     dataset_name = "cthead1"
     image = input_images[dataset_name]
-    baseline_name = "2_4/RFC3_GAUSSIAN.zarr"
+    baseline_name = "2_4_chunks64/RFC3_GAUSSIAN.zarr"
     chunks = (64, 64)
     multiscales = to_multiscales(
         image, [2, 4], chunks=chunks, method=Methods.ITKWASM_GAUSSIAN
diff --git a/py/test/test_to_ngff_zarr_tensorstore.py b/py/test/test_to_ngff_zarr_tensorstore.py
index f951045e..e97ef3e0 100644
--- a/py/test/test_to_ngff_zarr_tensorstore.py
+++ b/py/test/test_to_ngff_zarr_tensorstore.py
@@ -53,18 +53,25 @@ def test_gaussian_isotropic_scale_factors(input_images):
     baseline_name = "2_3/ITKWASM_GAUSSIAN.zarr"
     multiscales = to_multiscales(image, [2, 3], method=Methods.ITKWASM_GAUSSIAN)
     with tempfile.TemporaryDirectory() as tmpdir:
-        to_ngff_zarr(tmpdir, multiscales, use_tensorstore=True)
+        # The baselines hold the exact geometry. The default "pad" strategy
+        # trades it for speed and is covered in
+        # test_non_power_of_2_scale_factors.py.
+        to_ngff_zarr(tmpdir, multiscales, use_tensorstore=True, scale_strategy="exact")
         multiscales = from_ngff_zarr(tmpdir)
-        verify_against_baseline(dataset_name, baseline_name, multiscales)
+        verify_against_baseline(
+            dataset_name, baseline_name, multiscales, scale_strategy="exact"
+        )
 
     dataset_name = "MR-head"
     image = input_images[dataset_name]
     baseline_name = "2_3_4/ITKWASM_GAUSSIAN.zarr"
     multiscales = to_multiscales(image, [2, 3, 4], method=Methods.ITKWASM_GAUSSIAN)
     with tempfile.TemporaryDirectory() as tmpdir:
-        to_ngff_zarr(tmpdir, multiscales, use_tensorstore=True)
+        to_ngff_zarr(tmpdir, multiscales, use_tensorstore=True, scale_strategy="exact")
         multiscales = from_ngff_zarr(tmpdir)
-        verify_against_baseline(dataset_name, baseline_name, multiscales)
+        verify_against_baseline(
+            dataset_name, baseline_name, multiscales, scale_strategy="exact"
+        )
 
 
 @pytest.mark.skipif(
@@ -232,6 +239,80 @@ def test_tensorstore_chunk_shape_consistency_with_sharding():
         )
 
 
+def _write_and_read_codecs(tmpdir, **to_ngff_zarr_kwargs):
+    import json
+
+    data = np.arange(64 * 64, dtype=np.uint16).reshape(64, 64)
+    image = to_ngff_image(data, dims=("y", "x"), scale={"y": 1.0, "x": 1.0})
+    multiscales = to_multiscales(image, [2], method=Methods.DASK_IMAGE_GAUSSIAN)
+    to_ngff_zarr(
+        tmpdir,
+        multiscales,
+        use_tensorstore=True,
+        version="0.5",
+        **to_ngff_zarr_kwargs,
+    )
+    array_metadata = json.loads(
+        (pathlib.Path(tmpdir) / "scale0" / "image" / "zarr.json").read_text()
+    )
+    return array_metadata["codecs"]
+
+
+@pytest.mark.skipif(
+    zarr_version < version.parse("3.0.8"), reason="zarr version < 3.0.0b1"
+)
+def test_tensorstore_codec_chain_preserved():
+    """An explicit codec chain is written in order, not reduced to one codec."""
+    pytest.importorskip("tensorstore")
+    from zarr.codecs import BytesCodec, GzipCodec, ZstdCodec
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        codecs = _write_and_read_codecs(
+            tmpdir,
+            compressors=[BytesCodec(), ZstdCodec(level=3), GzipCodec(level=2)],
+        )
+    assert [codec["name"] for codec in codecs] == ["bytes", "zstd", "gzip"]
+    assert codecs[1]["configuration"]["level"] == 3
+    assert codecs[2]["configuration"]["level"] == 2
+
+
+@pytest.mark.skipif(
+    zarr_version < version.parse("3.0.8"), reason="zarr version < 3.0.0b1"
+)
+def test_tensorstore_codec_chain_bytes_only():
+    """A chain holding only the bytes codec writes uncompressed data."""
+    pytest.importorskip("tensorstore")
+    from zarr.codecs import BytesCodec
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        codecs = _write_and_read_codecs(tmpdir, compressors=[BytesCodec()])
+    assert [codec["name"] for codec in codecs] == ["bytes"]
+
+
+@pytest.mark.skipif(
+    zarr_version < version.parse("3.0.8"), reason="zarr version < 3.0.0b1"
+)
+def test_tensorstore_codec_chain_empty():
+    """An explicit empty chain writes uncompressed data."""
+    pytest.importorskip("tensorstore")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        codecs = _write_and_read_codecs(tmpdir, compressors=())
+    assert [codec["name"] for codec in codecs] == ["bytes"]
+
+
+@pytest.mark.skipif(
+    zarr_version < version.parse("3.0.8"), reason="zarr version < 3.0.0b1"
+)
+def test_tensorstore_codec_chain_default():
+    """Without a codec option the default zstd compression is applied."""
+    pytest.importorskip("tensorstore")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        codecs = _write_and_read_codecs(tmpdir)
+    assert [codec["name"] for codec in codecs] == ["bytes", "zstd"]
+
+
 @pytest.mark.skipif(
     zarr_version < version.parse("3.0.8"), reason="zarr version < 3.0.0b1"
 )
diff --git a/py/test/test_to_ngff_zarr_v3_compression.py b/py/test/test_to_ngff_zarr_v3_compression.py
index 38b186c7..24e53b7d 100644
--- a/py/test/test_to_ngff_zarr_v3_compression.py
+++ b/py/test/test_to_ngff_zarr_v3_compression.py
@@ -22,7 +22,7 @@
 def test_zarr_v3_compression(input_images):
     dataset_name = "cthead1"
     image = input_images[dataset_name]
-    baseline_name = "2_4/RFC3_GAUSSIAN.zarr"
+    baseline_name = "2_4_chunks64/RFC3_GAUSSIAN.zarr"
     chunks = (64, 64)
     multiscales = to_multiscales(
         image, [2, 4], chunks=chunks, method=Methods.ITKWASM_GAUSSIAN
@@ -72,7 +72,7 @@ def test_zarr_v3_compression_with_sharding(input_images):
     """Test Zarr v3 compression combined with sharding functionality"""
     dataset_name = "cthead1"
     image = input_images[dataset_name]
-    baseline_name = "2_4/RFC3_GAUSSIAN.zarr"
+    baseline_name = "2_4_chunks64/RFC3_GAUSSIAN.zarr"
     chunks = (64, 64)
     multiscales = to_multiscales(
         image, [2, 4], chunks=chunks, method=Methods.ITKWASM_GAUSSIAN
diff --git a/py/test/test_writer_isolation.py b/py/test/test_writer_isolation.py
new file mode 100644
index 00000000..2e73f8de
--- /dev/null
+++ b/py/test/test_writer_isolation.py
@@ -0,0 +1,150 @@
+# SPDX-FileCopyrightText: Copyright (c) Fideus Labs LLC
+# SPDX-License-Identifier: MIT
+"""ngff-zarr should not inherit backend defaults, nor leak into its caller.
+
+The baseline comparison works on decompressed values, so neither of these
+shows up there. They need their own tests.
+"""
+
+import json
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pytest
+import zarr
+from ngff_zarr import config, to_multiscales, to_ngff_image, to_ngff_zarr
+from packaging import version
+
+zarr_version = version.parse(zarr.__version__)
+
+pytestmark = pytest.mark.skipif(
+    zarr_version < version.parse("3.0.0b1"), reason="zarr version < 3.0.0b1"
+)
+
+
+def _multiscales(shape=(128, 256, 256), dtype=np.uint16):
+    image = to_ngff_image(np.zeros(shape, dtype=dtype), dims=("z", "y", "x"))
+    return to_multiscales(image, [2])
+
+
+def _scale0_metadata(store_path):
+    return json.loads((Path(store_path) / "scale0" / "image" / "zarr.json").read_text())
+
+
+def test_tensorstore_matches_zarr_python_by_default():
+    """Left unset, TensorStore applies its own defaults: sharded, uncompressed."""
+    pytest.importorskip("tensorstore")
+    ms = _multiscales()
+
+    written = {}
+    for use_tensorstore in (False, True):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            to_ngff_zarr(tmpdir, ms, version="0.5", use_tensorstore=use_tensorstore)
+            meta = _scale0_metadata(tmpdir)
+            written[use_tensorstore] = (
+                meta["chunk_grid"]["configuration"]["chunk_shape"],
+                [codec["name"] for codec in meta["codecs"]],
+            )
+
+    assert written[True] == written[False], (
+        f"backends disagree: zarr-python wrote {written[False]}, "
+        f"tensorstore wrote {written[True]}"
+    )
+    assert "sharding_indexed" not in written[True][1], (
+        "sharding was never requested via chunks_per_shard"
+    )
+
+
+@pytest.mark.parametrize(
+    # No shuffle/typesize given: zarr-python evolves them from the dtype,
+    # and the TensorStore path must land on the same configuration.
+    "explicit_shuffle",
+    [True, False],
+    ids=["explicit-shuffle", "evolved-from-dtype"],
+)
+def test_tensorstore_honours_requested_compressor(explicit_shuffle):
+    """A supplied codec must reach the store with the same configuration
+    zarr-python would write, not just the same name."""
+    pytest.importorskip("tensorstore")
+    # Import from the submodule: recent zarr no longer re-exports the codec
+    # classes from zarr.codecs.
+    from zarr.codecs.blosc import BloscCodec, BloscShuffle
+
+    kwargs = {"cname": "zlib", "clevel": 5}
+    if explicit_shuffle:
+        kwargs["shuffle"] = BloscShuffle.shuffle
+    compressors = BloscCodec(**kwargs)
+    ms = _multiscales()
+
+    written = {}
+    for use_tensorstore in (False, True):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            to_ngff_zarr(
+                tmpdir,
+                ms,
+                version="0.5",
+                use_tensorstore=use_tensorstore,
+                compressors=compressors,
+            )
+            codecs = _scale0_metadata(tmpdir)["codecs"]
+            written[use_tensorstore] = next(
+                (codec for codec in codecs if codec["name"] == "blosc"), None
+            )
+
+    assert written[False] is not None, "zarr-python did not write blosc"
+    assert written[True] == written[False], (
+        f"backends disagree: zarr-python wrote {written[False]}, "
+        f"tensorstore wrote {written[True]}"
+    )
+
+
+def test_to_multiscales_leaves_its_input_alone():
+    """Callers reuse images across calls; results must not depend on history."""
+    image = to_ngff_image(np.zeros((512, 512), dtype=np.uint8), dims=("y", "x"))
+    data_before = image.data
+    callbacks_before = list(image.computed_callbacks)
+
+    to_multiscales(image, [2, 4], chunks=(64, 64))
+
+    assert image.data is data_before, "to_multiscales replaced the caller's data"
+    assert image.computed_callbacks == callbacks_before, (
+        "to_multiscales appended to the caller's computed_callbacks"
+    )
+
+
+def test_to_multiscales_does_not_leak_cache_cleanup():
+    """The large-image path registers a cleanup callback; keep it local."""
+    original_target = config.memory_target
+    config.memory_target = int(1e5)
+    try:
+        image = to_ngff_image(np.zeros((512, 512), dtype=np.uint8), dims=("y", "x"))
+        before = len(image.computed_callbacks)
+        to_multiscales(image, [2])
+        assert len(image.computed_callbacks) == before, (
+            "cache cleanup piled up on the caller's image"
+        )
+    finally:
+        config.memory_target = original_target
+
+
+def test_to_ngff_zarr_leaves_multiscales_alone():
+    """The write loop swaps data for on-disk views and regenerates scales;
+    none of that may reach the caller's multiscales."""
+    image = to_ngff_image(
+        np.zeros((120, 120), dtype=np.uint8), dims=("y", "x"), scale={"y": 1, "x": 1}
+    )
+    ms = to_multiscales(image, [2, 3, 4])
+    data_before = [img.data for img in ms.images]
+    shapes_before = [img.data.shape for img in ms.images]
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        to_ngff_zarr(tmpdir, ms, version="0.5")
+
+    for i, img in enumerate(ms.images):
+        assert img.data is data_before[i], (
+            f"to_ngff_zarr replaced the caller's data at scale {i}"
+        )
+        assert img.data.shape == shapes_before[i], (
+            f"to_ngff_zarr reshaped the caller's scale {i}"
+        )
diff --git a/ts/deno.json b/ts/deno.json
index 6f50689f..44323459 100644
--- a/ts/deno.json
+++ b/ts/deno.json
@@ -28,7 +28,7 @@
   },
   "imports": {
     "@itk-wasm/compare-images": "npm:@itk-wasm/compare-images@^5.4.1",
-    "@itk-wasm/downsample": "npm:@itk-wasm/downsample@^1.8.1",
+    "@itk-wasm/downsample": "npm:@itk-wasm/downsample@^2.0.0",
     "@itk-wasm/image-io": "npm:@itk-wasm/image-io@^1.6.0",
     "@std/assert": "jsr:@std/assert@^1.0.18",
     "@std/cli": "jsr:@std/cli@^1.0.27",
diff --git a/ts/deno.lock b/ts/deno.lock
index 6ceb5f73..3f10e1df 100644
--- a/ts/deno.lock
+++ b/ts/deno.lock
@@ -24,11 +24,11 @@
     "npm:@fideus-labs/fizarrita@^1.3.0": "1.3.0_zarrita@0.6.1",
     "npm:@fideus-labs/worker-pool@1": "1.0.0",
     "npm:@itk-wasm/compare-images@^5.4.1": "5.4.1",
-    "npm:@itk-wasm/downsample@^1.8.1": "1.8.1",
+    "npm:@itk-wasm/downsample@2": "2.0.0",
     "npm:@itk-wasm/image-io@^1.6.0": "1.6.0",
     "npm:@playwright/test@^1.58.1": "1.58.2",
     "npm:fflate@~0.8.2": "0.8.2",
-    "npm:itk-wasm@^1.0.0-b.196": "1.0.0-b.196",
+    "npm:itk-wasm@^1.0.0-b.196": "1.0.0-b.199",
     "npm:microdiff@^1.5.0": "1.5.0",
     "npm:numcodecs@~0.3.2": "0.3.2",
     "npm:reference-spec-reader@0.2": "0.2.0",
@@ -200,8 +200,8 @@
       ],
       "bin": true
     },
-    "@itk-wasm/downsample@1.8.1": {
-      "integrity": "sha512-8j29GkeIECmMgS70DyLHnLMT81Ua5HZ28xSifhshEK2nERq6x4V8nm+CjJDpnKpNE3ZFe3cIY/MES5lr4iF/FQ==",
+    "@itk-wasm/downsample@2.0.0": {
+      "integrity": "sha512-3EIeESnFE17i818xFSBO47tyJxgtDLhOkSJlsFixysO9jM2guBYuFD7u1rifAbacPeXb4zVhkyVDVO/78FzVwg==",
       "dependencies": [
         "itk-wasm"
       ]
@@ -317,6 +317,12 @@
     "actor@2.3.1": {
       "integrity": "sha512-ST/3wnvcP2tKDXnum7nLCLXm+/rsf8vPocXH2Fre6D8FQwNkGDd4JEitBlXj007VQJfiGYRQvXqwOBZVi+JtRg=="
     },
+    "agent-base@6.0.2": {
+      "integrity": "sha512-RZNwNclF7+MS/8bDg70amg32dyeZGZxiDuQmZxKLAlQjr3jGyLx+4Kkk58UO7D2QdgFIQCovuSuZESne6RG6XQ==",
+      "dependencies": [
+        "debug"
+      ]
+    },
     "asynckit@0.4.0": {
       "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q=="
     },
@@ -326,11 +332,12 @@
         "possible-typed-array-names"
       ]
     },
-    "axios@1.13.5": {
-      "integrity": "sha512-cz4ur7Vb0xS4/KUN0tPWe44eqxrIu31me+fbang3ijiNscE129POzipJJA6zniq2C/Z6sJCjMimjS8Lc/GAs8Q==",
+    "axios@1.19.0": {
+      "integrity": "sha512-ht/iuYZXEjFxLH/Hkezgd7m6JKlHHXEUSneaDz8uZe1Gj5QZtCnpyDsckvAiEnT89OEbCLmnte4R4sn7P0EKFw==",
       "dependencies": [
         "follow-redirects",
         "form-data",
+        "https-proxy-agent",
         "proxy-from-env"
       ]
     },
@@ -433,6 +440,12 @@
     "core-util-is@1.0.3": {
       "integrity": "sha512-ZQBvi1DcpJ4GDqanjucZ2Hj3wEO5pZDS89BWbkcrvdxksJorwUDDZamX9ldFkp9aw2lmBDLgkObEA4DWNJ9FYQ=="
     },
+    "debug@4.4.3": {
+      "integrity": "sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA==",
+      "dependencies": [
+        "ms"
+      ]
+    },
     "decompress-tar@4.1.1": {
       "integrity": "sha512-JdJMaCrGpB5fESVyxwpCx4Jdj2AagLmv3y58Qy4GE6HMVjWz1FeVQk1Ct4Kye7PftcdOo/7U7UKzYBJgqnGeUQ==",
       "dependencies": [
@@ -554,8 +567,8 @@
         "graceful-fs"
       ]
     },
-    "follow-redirects@1.15.11": {
-      "integrity": "sha512-deG2P0JfjrTxl50XGCDyfI97ZGVCxIpfKYmfyrQ54n5FO/0gfIES8C/Psl6kWVDolizcaaxZJnTS0QSMxvnsBQ=="
+    "follow-redirects@1.16.0": {
+      "integrity": "sha512-y5rN/uOsadFT/JfYwhxRS5R7Qce+g3zG97+JrtFZlC9klX/W5hD7iiLzScI4nZqUS7DNUdhPgw4xI8W2LuXlUw=="
     },
     "for-each@0.3.5": {
       "integrity": "sha512-dKx12eRCVIzqCxFGplyFKJMPvLEWgmNtUrpTiJIR5u97zEhRG8ySrtboPHZXx7daLxQVrl643cTzbab2tkQjxg==",
@@ -563,8 +576,8 @@
         "is-callable"
       ]
     },
-    "form-data@4.0.5": {
-      "integrity": "sha512-8RipRLol37bNs2bhoV67fiTEvdTrbMUYcFTiy3+wuuOnUog2QBHCZWXDRijWQfAkhBj2Uf5UnVaiWwA5vdd82w==",
+    "form-data@4.0.6": {
+      "integrity": "sha512-vKatAh4SlVfgbv+YtmhiRjhEMJsYpsG1Y2rMQtR+SVSbytsSD1YGzDIcrAJmdFec88u/+VoGmxnl+80gL1tRCQ==",
       "dependencies": [
         "asynckit",
         "combined-stream",
@@ -669,12 +682,19 @@
         "has-symbols"
       ]
     },
-    "hasown@2.0.2": {
-      "integrity": "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==",
+    "hasown@2.0.4": {
+      "integrity": "sha512-T2UbfbBEF32wiepXIsMlTW9+dDYC6wMh/t/vYA4tuOMKqWz/n3vr1NFSxQiyP+zk2mXsoMA/i/7qV6LKut1t1A==",
       "dependencies": [
         "function-bind"
       ]
     },
+    "https-proxy-agent@5.0.1": {
+      "integrity": "sha512-dFcAjpTQFgoLMzC2VwU+C/CbS7uRL0lWmxDITmqm7C+7F0Odmj6s9l6alZc6AELXhrnggM2CeWSXHGOdX2YtwA==",
+      "dependencies": [
+        "agent-base",
+        "debug"
+      ]
+    },
     "ieee754@1.2.1": {
       "integrity": "sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA=="
     },
@@ -820,8 +840,8 @@
     "it-stream-types@2.0.2": {
       "integrity": "sha512-Rz/DEZ6Byn/r9+/SBCuJhpPATDF9D+dz5pbgSUyBsCDtza6wtNATrz/jz1gDyNanC3XdLboriHnOC925bZRBww=="
     },
-    "itk-wasm@1.0.0-b.196": {
-      "integrity": "sha512-LJoGEFQLgE2lKERTEOq9XFtMTnPUwR0TBWxTA/hI1QPqvXfVNHQGv+vzHTrRCq2GAM+vtWZuVt/XecTBBkN6Hg==",
+    "itk-wasm@1.0.0-b.199": {
+      "integrity": "sha512-+02eJud1aRTlSrwInhsx8eWXqiIxWZYLeMy7pBegPY17YbSKBwr9lfMbY/XbnpyaleRdjq3r81yauciEvYZc5A==",
       "dependencies": [
         "@emnapi/wasi-threads",
         "@itk-wasm/dam",
@@ -907,6 +927,9 @@
     "mri@1.2.0": {
       "integrity": "sha512-tzzskb3bG8LvYGFF/mDTpq3jpI6Q9wc3LEmBaghu+DdCssd1FakN7Bc0hVNmEyGq1bq3RgfkCb3cmQLpNPOroA=="
     },
+    "ms@2.1.3": {
+      "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA=="
+    },
     "multiformats@13.4.2": {
       "integrity": "sha512-eh6eHCrRi1+POZ3dA+Dq1C6jhP1GNtr9CRINMb67OKzqW9I5DUuZM/3jLPlzhgpGeiNUlEGEbkCYChXMCc/8DQ=="
     },
@@ -1008,8 +1031,8 @@
         "uint8arrays"
       ]
     },
-    "proxy-from-env@1.1.0": {
-      "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg=="
+    "proxy-from-env@2.1.0": {
+      "integrity": "sha512-cJ+oHTW1VAEa8cJslgmUZrc+sjRKgAKl3Zyse6+PV38hZe/V6Z14TbCuXcan9F9ghlz4QrFr2c92TNF82UkYHA=="
     },
     "rabin-rs@2.1.0": {
       "integrity": "sha512-5y72gAXPzIBsAMHcpxZP8eMDuDT98qMP1BqSDHRbHkJJXEgWIN1lA47LxUqzsK6jknOJtgfkQr9v+7qMlFDm6g=="
@@ -1224,7 +1247,7 @@
       "npm:@fideus-labs/fizarrita@^1.3.0",
       "npm:@fideus-labs/worker-pool@1",
       "npm:@itk-wasm/compare-images@^5.4.1",
-      "npm:@itk-wasm/downsample@^1.8.1",
+      "npm:@itk-wasm/downsample@2",
       "npm:@itk-wasm/image-io@^1.6.0",
       "npm:@playwright/test@^1.58.1",
       "npm:fflate@~0.8.2",
diff --git a/ts/src/methods/itkwasm-browser.ts b/ts/src/methods/itkwasm-browser.ts
index 1636e824..8417561b 100644
--- a/ts/src/methods/itkwasm-browser.ts
+++ b/ts/src/methods/itkwasm-browser.ts
@@ -18,8 +18,11 @@ import type { ZarrCodec } from "../utils/codecs.ts";
 import { defaultCodecs } from "../utils/codecs.ts";
 import { zarrGet, zarrSet } from "../utils/worker_pool.ts";
 import {
+  castImageToFloat32,
+  castImageToIntegerType,
   type DimFactors,
   dimScaleFactors,
+  isIntegerComponentType,
   itkImageToZarr,
   MAX_VECTOR_COMPONENTS,
   nextScaleMetadata,
@@ -324,11 +327,22 @@ async function downsampleGaussian(
   // Use all zeros for cropRadius
   const cropRadius = new Array(shrinkFactors.length).fill(0);
 
+  // The Gaussian filter loses precision on integer inputs; cast to
+  // float32 and round back afterwards, mirroring the Python port.
+  const originalComponentType = itkImage.imageType.componentType;
+  const needsFloatWorkaround = isIntegerComponentType(originalComponentType);
+  const inputImage = needsFloatWorkaround
+    ? castImageToFloat32(itkImage)
+    : itkImage;
+
   // Perform downsampling using browser-compatible function
-  const { downsampled } = await downsample(itkImage, {
+  const { downsampled: downsampledRaw } = await downsample(inputImage, {
     shrinkFactors,
     cropRadius: cropRadius,
   });
+  const downsampled = needsFloatWorkaround
+    ? castImageToIntegerType(downsampledRaw, originalComponentType)
+    : downsampledRaw;
 
   // Compute new metadata
   const [translation, scale] = nextScaleMetadata(
diff --git a/ts/src/methods/itkwasm-node.ts b/ts/src/methods/itkwasm-node.ts
index a97b020d..0b496548 100644
--- a/ts/src/methods/itkwasm-node.ts
+++ b/ts/src/methods/itkwasm-node.ts
@@ -23,8 +23,11 @@ import type { ZarrCodec } from "../utils/codecs.ts";
 import { defaultCodecs } from "../utils/codecs.ts";
 import { zarrGet, zarrSet } from "../utils/worker_pool.ts";
 import {
+  castImageToFloat32,
+  castImageToIntegerType,
   type DimFactors,
   dimScaleFactors,
+  isIntegerComponentType,
   itkImageToZarr,
   MAX_VECTOR_COMPONENTS,
   nextScaleMetadata,
@@ -329,11 +332,22 @@ async function downsampleGaussian(
   // Use all zeros for cropRadius
   const cropRadius = new Array(shrinkFactors.length).fill(0);
 
+  // The Gaussian filter loses precision on integer inputs; cast to
+  // float32 and round back afterwards, mirroring the Python port.
+  const originalComponentType = itkImage.imageType.componentType;
+  const needsFloatWorkaround = isIntegerComponentType(originalComponentType);
+  const inputImage = needsFloatWorkaround
+    ? castImageToFloat32(itkImage)
+    : itkImage;
+
   // Perform downsampling using Node-compatible function
-  const { downsampled } = await downsample(itkImage, {
+  const { downsampled: downsampledRaw } = await downsample(inputImage, {
     shrinkFactors,
     cropRadius: cropRadius,
   });
+  const downsampled = needsFloatWorkaround
+    ? castImageToIntegerType(downsampledRaw, originalComponentType)
+    : downsampledRaw;
 
   // Compute new metadata
   const [translation, scale] = nextScaleMetadata(
diff --git a/ts/src/methods/itkwasm-shared.ts b/ts/src/methods/itkwasm-shared.ts
index c7029d87..ce941c97 100644
--- a/ts/src/methods/itkwasm-shared.ts
+++ b/ts/src/methods/itkwasm-shared.ts
@@ -6,6 +6,7 @@
  * Used by both browser and Node implementations
  */
 
+import { castImage } from "itk-wasm";
 import type { Image } from "itk-wasm";
 import * as zarr from "zarrita";
 
@@ -254,6 +255,102 @@ export function getItkComponentType(
   return "float32";
 }
 
+/**
+ * Integer component types eligible for the Gaussian float32 workaround
+ */
+export type IntegerComponentType =
+  | "uint8"
+  | "int8"
+  | "uint16"
+  | "int16"
+  | "uint32"
+  | "int32";
+
+const INTEGER_COMPONENT_RANGES: Record<
+  IntegerComponentType,
+  [number, number]
+> = {
+  uint8: [0, 255],
+  int8: [-128, 127],
+  uint16: [0, 65535],
+  int16: [-32768, 32767],
+  uint32: [0, 4294967295],
+  int32: [-2147483648, 2147483647],
+};
+
+export function isIntegerComponentType(
+  componentType: unknown,
+): componentType is IntegerComponentType {
+  return typeof componentType === "string" &&
+    Object.hasOwn(INTEGER_COMPONENT_RANGES, componentType);
+}
+
+/**
+ * Round half to even, matching NumPy's rint used by the Python port.
+ */
+export function rintHalfToEven(value: number): number {
+  const floor = Math.floor(value);
+  const frac = value - floor;
+  if (frac < 0.5) return floor;
+  if (frac > 0.5) return floor + 1;
+  return floor % 2 === 0 ? floor : floor + 1;
+}
+
+/**
+ * Cast an ITK-Wasm image to float32.
+ *
+ * itkwasm-downsample's Gaussian filter performs internal arithmetic that
+ * loses precision on integer inputs (e.g. a uint16 input of 1 produces 0).
+ * The Python port casts integer inputs to float32 before the Gaussian
+ * downsample, then rounds and casts back; this helper and
+ * castImageToIntegerType mirror that so both ports produce identical
+ * pixel values.
+ */
+export function castImageToFloat32(image: Image): Image {
+  if (image.data === null) {
+    throw new Error("Image data is null");
+  }
+  return castImage(image, { componentType: "float32" });
+}
+
+/**
+ * Round a float image back to the given integer type, clamping to the
+ * type's range. Matches np.clip(np.rint(data), min, max) in the Python
+ * port's float workaround.
+ *
+ * castImage performs the type conversion; it truncates raw floats, so the
+ * values are rounded and clamped first. The intermediate is float64 because
+ * float32 cannot represent every uint32/int32 value exactly.
+ */
+export function castImageToIntegerType(
+  image: Image,
+  componentType: IntegerComponentType,
+): Image {
+  const src = image.data;
+  if (src === null) {
+    throw new Error("Image data is null");
+  }
+  const [min, max] = INTEGER_COMPONENT_RANGES[componentType];
+  const rounded = new Float64Array(src.length);
+  for (let i = 0; i < src.length; i++) {
+    let value = rintHalfToEven(src[i] as number);
+    if (value < min) {
+      value = min;
+    } else if (value > max) {
+      value = max;
+    }
+    rounded[i] = value;
+  }
+  return castImage(
+    {
+      ...image,
+      imageType: { ...image.imageType, componentType: "float64" },
+      data: rounded,
+    },
+    { componentType },
+  );
+}
+
 /**
  * Create identity matrix for ITK direction
  */
diff --git a/ts/test/baseline_comparison_test.ts b/ts/test/baseline_comparison_test.ts
index 709417d2..98c32ead 100644
--- a/ts/test/baseline_comparison_test.ts
+++ b/ts/test/baseline_comparison_test.ts
@@ -14,7 +14,6 @@
 import { assertEquals, assertExists } from "@std/assert";
 import { join } from "@std/path";
 import { readImageNode, writeImageNode } from "@itk-wasm/image-io";
-import { compareImagesNode } from "@itk-wasm/compare-images";
 import type { Image as ItkWasmImage } from "itk-wasm";
 
 import { Methods } from "../src/types/methods.ts";
@@ -70,60 +69,72 @@ async function writeScaleImage(
 }
 
 /**
- * Helper to compare two ITK-Wasm images with tolerance
+ * Compare two ITK-Wasm images by value: geometry and pixel data must be
+ * identical. Mirrors the Python port's store_equals, which compares
+ * decompressed arrays element-wise, so both ports assert the same thing.
+ *
+ * @itk-wasm/compare-images is deliberately not used: the comparison wanted
+ * here is exact — zero threshold, zero pixels tolerance — which is plain
+ * element-wise equality, so a Wasm round trip would buy nothing. (It also
+ * threw on 3D images with a non-identity direction, fixed upstream in
+ * InsightSoftwareConsortium/ITK-Wasm#1582.)
  */
-async function compareImages(
+function compareImages(
   testImage: ItkWasmImage,
   baselineImage: ItkWasmImage,
   testName: string,
-): Promise<void> {
-  try {
-    const result = await compareImagesNode(testImage, {
-      baselineImages: [baselineImage],
-      differenceThreshold: 0.0,
-      radiusTolerance: 0,
-      numberOfPixelsTolerance: 0,
-      ignoreBoundaryPixels: false,
-    });
+): void {
+  assertEquals(testImage.size, baselineImage.size, `${testName}: size`);
+  assertEquals(
+    testImage.spacing,
+    baselineImage.spacing,
+    `${testName}: spacing`,
+  );
+  assertEquals(testImage.origin, baselineImage.origin, `${testName}: origin`);
+  assertEquals(
+    Array.from(testImage.direction as ArrayLike<number>),
+    Array.from(baselineImage.direction as ArrayLike<number>),
+    `${testName}: direction`,
+  );
+  assertEquals(
+    testImage.imageType.componentType,
+    baselineImage.imageType.componentType,
+    `${testName}: componentType`,
+  );
 
-    // Check if images match
-    const metrics = result.metrics as {
-      almostEqual?: boolean;
-      numberOfPixelsWithDifferences?: number;
-    };
+  const testData = testImage.data;
+  const baselineData = baselineImage.data;
+  assertExists(testData, `${testName}: test image data`);
+  assertExists(baselineData, `${testName}: baseline image data`);
+  assertEquals(
+    testData.length,
+    baselineData.length,
+    `${testName}: data length`,
+  );
 
-    if (!metrics.almostEqual) {
-      console.error(
-        `❌ ${testName} failed: ${metrics.numberOfPixelsWithDifferences} pixels differ`,
-      );
+  let diffCount = 0;
+  let diffSum = 0;
+  let firstDiffIndex = -1;
+  for (let i = 0; i < testData.length; i++) {
+    // Compare raw values (works for bigint arrays too); Number() only for
+    // the diagnostics.
+    if (testData[i] !== baselineData[i]) {
+      diffCount++;
+      diffSum += Number(testData[i]) - Number(baselineData[i]);
+      if (firstDiffIndex < 0) firstDiffIndex = i;
     }
-
-    assertEquals(
-      metrics.almostEqual,
-      true,
-      `Images should match for ${testName}`,
-    );
-    assertEquals(
-      metrics.numberOfPixelsWithDifferences,
-      0,
-      `No pixels should differ for ${testName}`,
+  }
+  if (diffCount > 0) {
+    const meanDiff = (diffSum / diffCount).toFixed(4);
+    console.error(
+      `❌ ${testName} failed: ${diffCount}/${testData.length} pixels differ, ` +
+        `mean diff ${meanDiff}, first at index ${firstDiffIndex} ` +
+        `(test=${testData[firstDiffIndex]}, baseline=${
+          baselineData[firstDiffIndex]
+        })`,
     );
-  } catch (error) {
-    console.error(`Error comparing images for ${testName}:`, error);
-    console.error("Test image info:", {
-      size: testImage.size,
-      spacing: testImage.spacing,
-      origin: testImage.origin,
-      dataLength: testImage.data?.length,
-    });
-    console.error("Baseline image info:", {
-      size: baselineImage.size,
-      spacing: baselineImage.spacing,
-      origin: baselineImage.origin,
-      dataLength: baselineImage.data?.length,
-    });
-    throw error;
   }
+  assertEquals(diffCount, 0, `No pixels should differ for ${testName}`);
 }
 
 Deno.test("cthead1 - ITKWASM_GAUSSIAN scale factors [2, 4]", async () => {
@@ -320,13 +331,10 @@ Deno.test("MR-head - ITKWASM_GAUSSIAN scale factors [2, 3, 4]", async () => {
   const inputPath = join(INPUT_DIR, "MR-head.nrrd");
   const itkImage = await readImageNode(inputPath);
   assertExists(itkImage);
-  // The baseline store predates automatic anatomical-orientation writing and
-  // carries no orientation, so its ITK direction is identity. Disable
-  // orientation here to compare downsampled pixel output apples-to-apples; the
-  // orientation round-trip itself is covered by rfc4_integration_test.ts.
-  const ngffImage = await itkImageToNgffImage(itkImage, {
-    addAnatomicalOrientation: false,
-  });
+  // The baseline store carries RFC-4 anatomical orientation, so keep it on
+  // the test side too; both are converted back to an ITK direction by
+  // ngffImageToItkImage before comparison.
+  const ngffImage = await itkImageToNgffImage(itkImage);
 
   // Generate multiscales
   const multiscales = await toMultiscales(ngffImage, {