Knowledge Distillation From End-To-End Image Compression To Vvc Intra Coding For Perceptual Quality Enhancement

Abstract

In the current hybrid coding schemes, mean-squared-error is widely used for the rate-distortion optimization, which leads to high peak signal-to-noise ratio but sub-optimal perceptual quality. Although human perception-related measures, like multi-scale structural similarity (MS-SSIM), have been proposed, plugging them into the hybrid coding schemes may be computationally expensive. Recently, end-to-end optimized image compression has demonstrated the advantage of perceptual quality-oriented optimization by simply changing the training loss function. Inspired by this, we propose to distill the "perceptual" knowledge from end-to-end image compression and use the knowledge to enhance the perceptual quality for Versatile Video Coding (VVC) intra coding. For an input image, we obtain the block-level bit allocation via end-to-end image compression, and use the bit allocation to adjust the quantization parameter of VVC intra coding. Being compatible to the VVC standard, our method achieves on average 9.32% BD-rate reduction on the Kodak image set when evaluated by MS-SSIM, compared to the VVC reference software.