Group Perceptual Quality Optimization for Multi-Channel Image Encoding Systems Based on Adaptive Hyper Networks

Images and short videos that produced by social networks surge in recent years. Image/Video encoders, such as JPEG and H.264, are indispensably involved to reduce the transmitting bandwidth. However, based on our observation, the encoding parameters and their candidates are often preset to fixed values (or fixed candidate values) in real-world scenarios, which might not be the optimal bandwidth allocation strategy. Considering that, we propose an efficient group quality optimization (GQO) framework for multi-channel image/video encoding systems in which the encoding parameters are configured in a perceptual-quality-driven manner. The GQO framework employs adaptive hyper network to predict the relationships between encoding parameters, transmitting resources, and perceptual qualities, i.e., just taking the pristine image as input, the adaptive hyper network could accurately yield a global overview of perceptual quality and transmitting resource varied along encoding parameters. A step-by-step optimization procedure is then employed to search the optimal encoding parameter for each channel so that overall perceptual quality could be maximized under limited transmitting resource. Experimental results demonstrate the proposed GQO framework could achieve higher perceptual quality whilst maintain the same bandwidth compared to traditional allocation strategies where encoding parameters are preset.

Automated summary

Images and short videos produced by social networks have experienced a surge in recent years, requiring the use of image/video encoders to reduce transmission bandwidth. However, in real-world scenarios, encoding parameters are often preset to fixed values, which may not be the optimal bandwidth allocation strategy. Therefore, an efficient group quality optimization framework has been proposed to optimize encoding parameters based on perceptual quality.