Image Deblurring Using Deep Multi-Scale Distortion Prior

Video analytics systems designed for computer vision tasks use deep learning models that rely on high-quality input data to maximize performance. However, in a real-world system, these inputs are often compressed using video codecs such as HEVC. Video compression degrades the quality of the inputs, thereby degrading the performance of these models. Region-of-interest (ROI) coding enables bits to be allocated to improve performance; however, the method to select regions should be computationally simple since it must occur during or before the video is compressed and transmitted for further processing. in this paper, we propose a task-aware quad-tree (TA-QT) partitioning and quantization method to achieve ROI coding for HEVC and other video coding standards. TA-QT uses a lightweight edge-based model to guide task-aware video encoding to improve end-stage video analytics (ESVA) performance while reducing both bit-rate and encoding time. We demonstrate the effectiveness of our approach in terms of (a) the performance of the ESVA on compressed inputs, (b) transmission bit-rates, and (c) encoding time.