Enhancing Quality for HEVC Compressed Videos

TL;DR

This paper introduces a CNN-based method to enhance the visual quality of HEVC compressed videos at the decoder side, improving both I and P frame quality without modifying the encoder.

Contribution

It proposes QE-CNN models for I and P frames, and a TQEO scheme for real-time quality enhancement under time constraints, which is novel compared to existing intra-only approaches.

Findings

Effective enhancement of HEVC I and P frames quality.

TQEO scheme achieves accurate time control and quality improvement.

Prototype demonstrates real-time applicability.

Abstract

The latest High Efficiency Video Coding (HEVC) standard has been increasingly applied to generate video streams over the Internet. However, HEVC compressed videos may incur severe quality degradation, particularly at low bit-rates. Thus, it is necessary to enhance the visual quality of HEVC videos at the decoder side. To this end, this paper proposes a Quality Enhancement Convolutional Neural Network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC. In particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the distortion of HEVC I and P frames, respectively. The proposed method differs from the existing CNN-based quality enhancement approaches, which only handle intra-coding distortion and are thus not suitable for P frames. Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos. To apply our QE-CNN method in time-constrained scenarios, we further propose a Time-constrained Quality Enhancement Optimization (TQEO) scheme. Our TQEO scheme controls the computational time of QE-CNN to meet a target, meanwhile maximizing the quality enhancement. Next, the experimental results demonstrate the effectiveness of our TQEO scheme from the aspects of time control accuracy and quality enhancement under different time constraints. Finally, we design a prototype to implement our TQEO scheme in a real-time scenario.