v0.17.0

Release Highlights

CV-CUDA v0.17.0-beta includes the following changes:

New Features and Enhancements

• Added the JpegCompressionDistortion operator, a self-contained GPU simulation of JPEG compression artifacts (full-range JFIF YCbCr, 4:2:0 chroma subsampling, per-8x8-block DCT quantization; ported from NVIDIA DALI, no codec dependency) with per-image or per-batch quality.

• Added the AdjustContrast, AdjustHue, AdjustSaturation, AdjustSharpness, AutoContrast, CLAHE (Contrast Limited Adaptive Histogram Equalization), Invert, Posterize, and Solarize operators.

• Added native planar (NCHW/CHW) support across the public image operator catalog, subject to each operator's documented input type, dimensionality, channel, format, and auxiliary-input limitations.

• Added tensor input support to GammaContrast and by-value parameter paths to BrightnessContrast, GammaContrast, GaussianNoise, and Normalize, avoiding temporary device parameter tensors and host-to-device copies.

• Added NVCVRoundMode (cvcuda.Round in Python), with nearest and truncate modes for integral output conversion in ConvertTo and the scalar GammaContrast path.

• Extended the Python API with Stream.wait_stream(), NumPy float16 interoperability, single-dimension and empty TensorLayout values, and hashable TensorLayout and Type objects.

• Added always-on NVTX ranges to public C and Python operator submissions for improved Nsight profiling visibility.

• Expanded Python operator samples and documentation, and revamped the Docker build and development images. Source builds no longer use Git submodules.

Performance Improvements

• Improved performance across more than 50 operators, with 1.8x average speedups on H100.

• Reduced Python wrapper overhead in ResourceGuard with batched sync/hold and cached pybind11 reverse casts.

• Reduced binary and package sizes with size-optimized CUDA fatbinary compression when built with CUDA 12.8 or later.

• Reduced x86_64 package sizes by removing blanket SM86 and SM89 code generation while retaining performance-sensitive operator cubins and conservative coverage for inconclusive or post-freeze cases.

Bug Fixes

• Fixed correctness and robustness issues across operators: Composite, Erase, FindHomography, GammaContrast, GaussianNoise, Histogram, HistogramEq, HQResize, Inpaint, PillowResize, RandomResizedCrop, ResizeCropConvertReformat, Rotate, and WarpAffine/WarpPerspective.

• Fixed cubic interpolation boundary conditions in Resize and the shared cubic paths used by RandomResizedCrop, and added 1-channel (grayscale/Y8) support to ResizeCropConvertReformat (issue [#285]).

• Fixed multi-GPU workspace and cache handling, CUDA Array Interface and DLPack stream synchronization, device restoration during Stream destruction, and memory growth in the as_image cache (issue [#258]).

• Strengthened input validation and memory safety across operators, returning clear errors instead of aborting or launching invalid CUDA work. Also fixed ResourceGuard error paths that could terminate the Python process.

• Fixed text rendering backend issues and added dedicated unit coverage for the STB text backend.

• Corrected multi-stream Python test synchronization (community contribution PR [#288] by Daniel Ching).

Compatibility Changes

• Dropped official support for Python 3.9.

• C++ operator wrapper classes are now move-only; copying an operator wrapper no longer compiles.

• The C cvcudaConvertToSubmit function now requires an explicit NVCVRoundMode argument. C++ and Python callers retain nearest rounding as the default; existing C binaries using this entry point must be rebuilt.

• Public NVCV allocator callbacks and resource user-pointer APIs now use the opaque NVCVResourceContext, NVCVMemoryBuffer, and NVCVUserPointer types instead of void *. Custom allocators and direct C/C++ callers may require signature updates or explicit casts.

• Added CVCUDA_AARCH64_JETSON for Jetson Orin source builds; default aarch64 source builds target SBSA.

Compatibility and Known Limitations

For full details, see the main README's compatibility table and known limitations.

License

CV-CUDA is licensed under the Apache 2.0 license.

Resources

1. CV-CUDA GitHub
2. Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries
3. Accelerating AI Pipelines: Boosting Visual Search Efficiency
4. Optimize Short-Form Video Processing Toward the Speed of Light
5. CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA
6. NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI
7. CV-CUDA Helps Tencent Cloud Audio and Video PaaS Platform Achieve Full-Process GPU Acceleration for Video Enhancement AI

Acknowledgements

CV-CUDA originated as a collaborative effort between NVIDIA and the ByteDance Machine Learning team.