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As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression.
On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.
#22 best model for Image Super-Resolution on Set5 - 4x upscaling
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.
SOTA for Image Compression on ImageNet32
During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.
We propose a context-adaptive entropy model for use in end-to-end optimized image compression.
We propose a new approach to the problem of optimizing autoencoders for lossy image compression.