Region-Adaptive Video Sharpening via Rate-Perception Optimization

TL;DR

This paper introduces RPO-AdaSharp, a region-adaptive video sharpening method that optimizes perceptual quality and bitrate by leveraging CTU partition masks for targeted enhancement.

Contribution

It proposes a novel end-to-end model that adaptively allocates bits for sharpening based on texture regions, improving quality and efficiency.

Findings

Effective qualitative and quantitative improvements

Better bitrate utilization for perceptual enhancement

Region-specific sharpening enhances overall video quality

Abstract

Sharpening is a widely adopted video enhancement technique. However, uniform sharpening intensity ignores texture variations, degrading video quality. Sharpening also increases bitrate, and there's a lack of techniques to optimally allocate these additional bits across diverse regions. Thus, this paper proposes RPO-AdaSharp, an end-to-end region-adaptive video sharpening model for both perceptual enhancement and bitrate savings. We use the coding tree unit (CTU) partition mask as prior information to guide and constrain the allocation of increased bits. Experiments on benchmarks demonstrate the effectiveness of the proposed model qualitatively and quantitatively.