Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
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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.
We introduce a simple and efficient lossless image compression algorithm.
We present a learned image compression system based on GANs, operating at extremely low bitrates.
We fully exploit the hierarchical features from all the convolutional layers.
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.
Ranked #2 on Image Compression on ImageNet32
This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs.
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.