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.

基于神经网络的图像压缩方法，提出了一种新的量化修正器（QR）方法，通过利用图像特征相关性降低量化对图像质量的影响，预测未量化的特征以提高图像重建质量，并在评估中与现有的神经图像编解码器进行对比，结果显示QR在编码效率方面有一致的提升并且运行时间几乎无明显增加。

神经图像压缩与量化整流器