The paper attributes the gap between ideal and practical lossy image coding to five effects, and reports an estimated rate-distortion upper bound that beats VTM by up to 35 percent on Kodak.
Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding
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abstract
Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework -- H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework -- iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding.
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The Gap Between Principle and Practice of Lossy Image Coding
The paper attributes the gap between ideal and practical lossy image coding to five effects, and reports an estimated rate-distortion upper bound that beats VTM by up to 35 percent on Kodak.