Edge-Based Video Compression Texture Synthesis Using Generative Adversarial Network

It has been recognized that texture patterns with abundant high-frequency components, such as grass and water, produce visual masking effects, and the distortion in textures is hard to be perceived by human eyes than structure regions. However, modern video codecs in a rate-distortion optimized manner usually consume a lot of bits to encode textures, leading to the insufficiency in perceptual coding performance. Nowadays, with the rapid development of deep learning, learning based texture synthesis methods have been proposed to replace the coding process of prediction residuals to reduce the rate cost. In this paper, we present a deep texture synthesizer named edge-based texture synthesis framework (ETSF). At encoder side, the framework detects texture regions by semantic and fidelity classification criteria, and the detected regions are quantized coarsely by the hybrid coding framework. In texture characterization, ETSF extracts low-level edge features representing pixel intensity variation. Feature processing tools are developed to remove the spatiotemporal redundancy of edges. The processed edge information is compressed and transmitted. To effectively recover textures, we design an edge-based texture synthesis generative adversarial network (ETSGAN) at the decoder of ETSF, which can incorporate edge information into convolutional layers and generate realistic textures. Experimental results on a collected texture dataset show that the proposed ETSF can achieve an average of -12.8%, -14.2% and -9.6% MOS BD-rate under lowdelay_B, lowdelay_P and random_access configurations of VVC coding, respectively.