Spatially Scalable Video Coding Based on Hybrid Epitomic Resizing

Abstract

Scalable video coding (SVC) is considered as a potentially promising solution to enable the adaptability of video to heterogonous networks and various devices. In spatially scalable video encoder, how to resize the captured high-resolution video to get low-resolution video has great effect on the quality of experience (QoE) in the clients receiving low-resolution video. In this paper, we propose a new resizing algorithm called hybrid epitomic resizing (HER), which can make the resized image preserve the same ‘physical’ resolution with original image by the way of utilizing texture similarity inside image and highlight regions of interest while avoiding potential artifacts. For hybrid epitomic resizing, we also design two new inter-layer prediction methods to eliminate the redundancy between adjacent spatial layers instead of conventional inter-layer prediction. Experimental results show that HER can get resized images with perceptually much better quality and the performance of new inter-layer prediction are comparable to that of conventional inter-layer prediction in H.264 SVC.