SA-CVSR: Scale-Arbitrary Compressed Video Super-Resolution

Abstract

To mitigate transmission and storage expenses, the existing compressed video super-resolution (CVSR) approaches typically downsample high-resolution (HR) videos before encoding and then restore decoded videos to their original resolution leveraging deep neural networks (DNNs). However, they employ fixed integer scale factors for diverse video types and compression ratios, potentially resulting in suboptimal performance. In this paper, we propose a Scale-Arbitrary Compressed Video Super-Resolution (SA-CVSR) approach to achieve the optimal trade-off between bit-rate and quality. In our approach, we first apply a Support Vector Machine (SVM) based scale predictor to determine the optimal scale factors for an individual video across various compression ratios. Then, we design a novel Priors-Guided Restoration–Reconstruction Network (PGRRN), which is constructed by stacking multiple Priors-Guided Processing Blocks (PGPBs), to process low-resolution (LR) compressed videos in two stages. Specifically, in the restoration stage, PGPBs perform precise motion compensation between two temporally adjacent frames and incorporate the coding prior, which enables PGRRN to effectively eliminate the compression damage content-adaptively. In the subsequent reconstruction stage, PGPBs incorporate the scale prior to achieve high-quality scale-arbitrary super-resolution. Extensive experimental results provide evidence of the effectiveness of SA-CVSR, as it demonstrates a substantial improvement in bit-rate reduction when compared to other CVSR approaches on multiple datasets.