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Contemporary lossy image and video coding standards rely on transform coding,the process through which pixels are mapped to an alternative representation tofacilitate efficient data compression. Despite impressive performance ofend-to-end optimized compression with deep neural networks, the highcomputational and space demands of these models has prevented them fromsuperseding the relatively simple transform coding found in conventional videocodecs. In this study, we propose learned transforms and entropy coding thatmay either serve as (non)linear drop-in replacements, or enhancements forlinear transforms in existing codecs. These transforms can be multi-rate,allowing a single model to operate along the entire rate-distortion curve. Todemonstrate the utility of our framework, we augmented the DCT with learnedquantization matrices and adaptive entropy coding to compress intra-frame AV1block prediction residuals. We report substantial BD-rate and perceptualquality improvements over more complex nonlinear transforms at a fraction ofthe computational cost.

Multi-rate adaptive transform coding for video compression