Compression of user generated content using denoised references

TL;DR

This paper proposes a novel approach for compressing user generated content by using denoised references to guide encoding, improving quality without altering original user content.

Contribution

It introduces a method that encodes UGC using denoised references for distortion measurement, enhancing compression quality while respecting user content integrity.

Findings

Improved JPEG compression quality for UGC images and videos.

Effective use of denoised references for perceptual quality enhancement.

Framework applicable to various compression standards.

Abstract

Video shared over the internet is commonly referred to as user generated content (UGC). UGC video may have low quality due to various factors including previous compression. UGC video is uploaded by users, and then it is re-encoded to be made available at various levels of quality. In a traditional video coding pipeline the encoder parameters are optimized to minimize a rate-distortion criterion, but when the input signal has low quality, this results in sub-optimal coding parameters optimized to preserve undesirable artifacts. In this paper we formulate the UGC compression problem as that of compression of a noisy/corrupted source. The noisy source coding theorem reveals that an optimal UGC compression system is comprised of optimal denoising of the UGC signal, followed by compression of the denoised signal. Since optimal denoising is unattainable and users may be against modification of their content, we propose encoding the UGC signal, and using denoised references only to compute distortion, so the encoding process can be guided towards perceptually better solutions. We demonstrate the effectiveness of the proposed strategy for JPEG compression of UGC images and videos.