Abstract
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.
Highlights
Recent years has witnessed the significant growth of social networks which produces massive amounts of images and short videos
The main contribution of our work is summarized as follow: (1) We propose an effective group quality optimizing framework for multi-channel image encoding systems by re-allocating the transmitting resources of each channel via Image Quality Assessment (IQA)-curves
Existing adaptive streaming techniques such as HTTP-DASH[6] are designed to fit various network environments, e.g., when the transmitting bandwidth are limited, the HTTP-DASH could switch to lower bit-rate to guarantee the fluence of the playback, whilst our proposed group quality optimization (GQO) framework are designed for the multi-channel encoding system to make full use of the limited storage and to yield optimal overall perceptual quality, i.e., the HTTP-DASH are driven by the transmitting environments but our GQO framework are driven by perceptual quality
Summary
Recent years has witnessed the significant growth of social networks which produces massive amounts of images and short videos. Such research is of importance because existing popular social networks often contain multiple images rather than single image in a webpage, video content providers have to compress multiple video streams simultaneously for users Encoding parameters such as Q value for JPEG image encoders and bitrate/QP for video encoders could not precisely represent the perceptual quality, incorporating IQA models as quality criteria and exploit a reasonable bandwidth re-allocating strategy based on such IQA quality score would benefit both the servers (saving transmitting resources) and clients (improving perceptual qualities). Existing adaptive streaming techniques such as HTTP-DASH[6] are designed to fit various network environments, e.g., when the transmitting bandwidth are limited, the HTTP-DASH could switch to lower bit-rate to guarantee the fluence of the playback, whilst our proposed GQO framework are designed for the multi-channel encoding system to make full use of the limited storage (or transmitting bandwidth) and to yield optimal overall perceptual quality, i.e., the HTTP-DASH are driven by the transmitting. The rest part is organized as follow: Section II introduces several related IQA works; Section III illustrates our proposed Group Quality Optimized framework and its specific version for multi-channel JPEG image encoding system; Section IV shows the experimental results; and Section V is conclusion
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