Region of Interest Coding Based on Convolutional Neural Network

Abstract The traditional region of interest coding method mainly uses low-level features to detect the Region of Interest (ROI). The ROI detected by it is poor in stability and is not easily interfered by noise. In this paper, ROI detection is performed on the image through a deep convolutional network to obtain a stable ROI based on the high-level feature extraction of the image, and then the discrete cosine transform (DCT) is performed on the image and divided into coding units. According to whether the coding unit is an ROI, To determine the quantization matrix used when encoding it. This article uses fine quantization for coding units that belong to ROI, and coarse quantization for non-ROI coding units. In this way, it can be ensured that the compression rate is greatly reduced without affecting the subjective perception of the image. Experiments show that the compression rate of this method can reach about 84%, and the weighted peak signal-to-noise ratio is improved by about 0. 99dB on average compared with JPEG encoding.