Comments (1)
Hi, thanks for trying out CompressAI!
Additive "uniform" noise (not Gaussian noise) is used during training to make the bottlenecks differentiable.
When testing, the rounding operations are only applied to the "centered" latent values (y-means_hat for example), this is more efficient compression wise when using range coding to work on centered distributions. During training, adding quantization noise to the latent or the "centered" latent is equivalent.
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Related Issues (20)
- When will the compressai 1.2.5 be submitted to PyPI?
- Adding new codec into CompressAI HOT 4
- Lambda values missing for neural codecs HOT 1
- The reconstructed image using codec.py is inconsistent with the original image HOT 2
- How can I finetune the large bitrate model (λ=0.05) to obtain the low bitrate model (i.e. λ=0.013) HOT 2
- training SSF2020 model with quality 8 HOT 1
- The Variable-Rate model training script HOT 1
- from compressai.ans import BufferedRansEncoder, RansDecoder HOT 1
- 'utf-8' UnicodeDecodeError: codec can't decode byte oxd6 in position 1: invalid continuation byte When export to ONNX HOT 1
- quality level argument missing in train.py? HOT 1
- Issue Regarding the Use of GaussianMixtureConditional HOT 6
- seems something worng with "python -m compressai.utils.eval_model --help " HOT 2
- Issue with Using CheckerboardMaskedConv2d from layer.py HOT 1
- sensetime and google HOT 8
- When I wanted to migrate that program to sequence signal data, I found that @register_model("mbt2018") The compress and decompress methods of class JointAutoregressiveHierarchicalPriors are very complex HOT 1
- On the Compatibility of GAN HOT 2
- Request ONNX export support for Cheng2020 model HOT 1
- error when trying to use entropy bottleneck of compressai in my code HOT 1
- Module Not Found Error: 'compressai' HOT 1
- How to convert compress and decompress into two ONNXs? Normally, compress and decompress are placed on two devices
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