CONTENT-ADAPTIVE RESOLUTION CONTROL TO IMPROVE VIDEO CODING EFFICIENCY

Aiming at improved rate-distortion (R-D) performance, this paper presents a machine-learning-based solution for the run-time video resolution adaptation problem. The proposed approach utilizes neural networks that leverage a complexity feature extracted from the video frames to predict a quantization parameter (QP) for downscaled video targeting the same bitrate as the native video. The peak signal to noise ratio (PSNR) is also predicted for both the native and downscaled resolutions, and the one that leads to the highest PSNR is selected. Experimental results show that the proposed adaptive approach achieves significant improvements in R-D performance compared to using a fixed resolution.