Texture and motion aware perception in-loop filter for AV1

Abstract

Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric.