ABSTRACT The quality fluctuation of video is significant in human visual system, and thus, many rate control schemes are widely developed in the area of video communication. In recent years, researchers show more interests in region of interest (ROI)-based encoding, and it is widely applied in the latest video codecs, such as HEVC and VP9. This paper presents a new rate control scheme for ROI mode coding based on discrete fourier transform coefficient model and radial basis function neuron network. A new R-D model is proposed by classifying blocks into different depth, ROI groups, and so on. Then, rate and distortion are described based on the Laplacian distribution model using mathematical ways. A machine learning approach is induced to enhance the accuracy of the distortion estimation. By utilizing the new R-D model, a new rate control scheme is designed for ROI mode coding from the group of picture layer to coding unit layer. By comparisons with other rate control approaches, the proposed one has a better result in terms of visual quality, R-D performance, bitrate accuracy, and so on. Hence, it outperforms the conventional schemes especially for sequences with obvious ROI details. INDEX TERMS Video coding, VP9, HEVC, region of interest, rate control, Laplacian distribution model, rate-optimization model, radial basis function neuron network, machine learning.
Reference REF proposes a rate control scheme for region of interest (ROI) based on Fourier transform and neuron network.
A New Rate Control Scheme For Video Coding Based On Region Of Interest
IEEE Access
IEEE Access
Computer Science