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Some of my notes on traditional compression in ./JPEG Compression.ipynb
Architecture used:
Can be trained like:
python train.py --epochs 50 --lr 0.0002 --logs logs/
Method achieved a 75.3% SSIM on the test split of the CLIC dataset.
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Paper doesn't explicitly mention this but using normalization of the rgb values helped with training
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Making the output of the
Cr
network go through asigmoid
before going intoCo
gave better results and makes sense for the gradients to flow although not mentioned in the paper -
Some of the notation of
Co(x_hat)
in the paper is confusing when showing the residual loss, using eq. (5) works fine -
Because of the
"same"
padding used in the architecture, seeing somme ugly border artifacts in the reconstructed images
General idea follows:
More details on the model were excluded from the paper and instead kept at a site that is no longer up. Wayback machine cache of it here.
N/A
No training code by the authors and paper description is very brief. Not able to reproduce for now.
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An End-to-End Compression Framework Based on Convolutional Neural Networks: encoder/decoder architecture tries to learn a compact, image-like representation of an image and use an image codec to store it, then uses interpolation + decoder network in the reconstruction
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Better Compression with Deep Pre-Editing: Learns a transform on the original image such that it can be modified into a "similar" image that can be compressed by a traditional codec but with less artifacts.
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Learning Convolutional Networks for Content-weighted Image Compression: encoder/decoder architecture that extracts a spatial importance mask to predict optimal bit allocation
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High-Fidelity Generative Image Compression: google paper on GANs for compression (code)
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Deep Generative Models for Distribution-Preserving Lossy Compression: training GANs with flexible bitrate. The resulting models behave like generative models at zero bitrate, almost perfectly reconstruct the training data at high enough bitrate (code)
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Full Resolution Image Compression with Recurrent Neural Networks: google paper on using different kinds of RNNs (blog)
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Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks: google paper that uses RNNs to progressively reconstruct an image