Optimization of video coding for object detection and perceptual quality

In order to reduce the impact of video coding distortion on object detection， an optimization method of ideo coding for object detection and perceptual quality was proposed. Firstly， the quantization parameter of I frame was refined to improve the video coding performance in terms of rate-compression-distortion. Secondly， the object detection algorithm was introduced into video codec to predict the object area of current coding frame. Thirdly， a commonly used deep neural network was utilized to extract the feature of current coding unit， which was used to calculate feature distortion. Then， a modified VGG model was proposed to predict the quantization parameter of current coding unit. Finally， the feature distortion and compression distortion were considered as joint distortion in rate-distortion optimization problem， in which the optimal coding parameters were decided. Experimental results showed that， compared with VTM-23.0， the proposed method could achieve about 10.5% BD-rate savings on object detection accuracy and about 2.2% BD-rate savings on compression distortion， respectively.