AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics
Tingting Yuan, Liang Mi, Weijun Wang, Haipeng Dai, Xiaoming Fu

TL;DR
AccDecoder is a real-time video decoding method that adaptively selects frames for neural enhancement using deep reinforcement learning, significantly improving accuracy and reducing latency in neural-enhanced video analytics.
Contribution
It introduces an adaptive frame selection approach with DRL for neural super-resolution, achieving faster and more accurate video analytics.
Findings
Achieves 6-21% accuracy improvement.
Reduces latency by 20-80% compared to baselines.
Enables real-time neural-enhanced video analytics.
Abstract
The quality of the video stream is key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor quality cameras or over-compressed/pruned video streaming protocols, e.g., as a result of upstream bandwidth limit. To address this issue, existing studies use quality enhancers (e.g., neural super-resolution) to improve the quality of videos (e.g., resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural-enhanced video analytics. AccDecoder can select a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Sparse and Compressive Sensing Techniques
