CompressAI
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A PyTorch library and evaluation platform for end-to-end compression research
I've configured Compressai on both Ubuntu and Windows. they both encode and decode fine on their respective systems. But the bin files compressed by Ubuntu are not decoded properly in...
Hello! I'm trying to train my own custom autoencoder model while integrating `EntropyBottleneck` and `GaussianConditional`. Here's a snippet of my class: ``` class AEWithEntropy(nn.Module): def __init__(self, freeze_base=True): super().__init__() self.base =...
 I've been stuck here all along, and soon the following error will appear: This error originates from a subprocess, and is likely not a problem with pip.
Sorry to bother you, but I have some stupid questions. I noticed that the `_compress_ar` function in the `JointAutoregressiveHierarchicalPriors` model uses two for-loops during the encoding process to predict the...
I want to know how can I get the result like the picture in the project. I notice that some of them can exceed 40dB, but i can just only...
I want to know the parameters accepted by EntropyBottleneck and GaussianConditional and their processing. Can you explain it a little bit? And what is the EntropyCoder used in this project?...
## Feature Please allow user to specify whether y_hat (and possibly also y) be returned from the forward and compress methods of all codec families. i.e.: ``` class SimpleVAECompressionModel.forward(CompressionModel): ......
https://github.com/InterDigitalInc/CompressAI/blob/master/compressai/utils/bench/codecs.py#L821 'img' is a file address for an image here, but this line of code treats it as data for the image The correct code should be: `arr = np.asarray(self._load_img(img))`
I created a function to normalize a 12-bit image to the range [0,1] and use it as input for network. I wonder whether this is sufficient or if certain parameters...
Hello, I have a question about **y_bpp** and normalization. In the implementation of the Gaussian entropy model in CompressAI, **y_bpp** is computed by estimating the likelihood after normalizing the input:...