Neural Image Compression with Quantization Rectifier
<|reference_start|>Neural Image Compression with Quantization Rectifier: Neural image compression has been shown to outperform traditional image codecs in terms of rate-distortion performance. However, quantization introduces errors in the compression process, which can degrade the quality of the compressed image. Existing approaches address the train-test mismatch problem incurred during quantization, the random impact of quantization on the expressiveness of image features is still unsolved. This paper presents a novel quantization rectifier (QR) method for image compression that leverages image feature correlation to mitigate the impact of quantization. Our method designs a neural network architecture that predicts unquantized features from the quantized ones, preserving feature expressiveness for better image reconstruction quality. We develop a soft-to-predictive training technique to integrate QR into existing neural image codecs. In evaluation, we integrate QR into state-of-the-art neural image codecs and compare enhanced models and baselines on the widely-used Kodak benchmark. The results show consistent coding efficiency improvement by QR with a negligible increase in the running time.<|reference_end|>
@article{luo2024neural,
    title={Neural Image Compression with Quantization Rectifier},
    author={Wei Luo, Bo Chen},
    journal={arXiv preprint arXiv:2403.17236},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17236},
    primaryClass={cs.LG}
}