An efficient quality metric for scalable video coding

Abstract

Scalable Video Coding (SVC) is one of the promising techniques to ensure Quality of Service (QoS) in multimedia communication through heterogeneous networks. SVC compresses a raw video into multiple bitstreams composed of a base bitstream and enhancement bitstreams to support multi scalabilities such as SNR, temporal and spatial. Therefore, it is able to extract an appropriate bitstream from original coded bitstream without re-encoding to adapt a video to user environment. In this flexible environment, QoS has appeared as an important issue for service acceptability. Therefore, there has been a need for measuring a degree of video quality to guarantee the quality of video streaming service. Existing studies on the video quality metric have mainly focused on temporal and SNR scalability. In this thesis, we propose an efficient quality metric, which allows for spatial scalability as well as temporal and SNR scalability. To this end, we study the effect of frame rate, SNR, spatial scalability and motion characteristics by using the subjective quality assessment, and then a new video quality metric supporting full scalabilities is proposed. Experimental results show that this quality metric has high correlation with subjective quality. Because the proposed metric is able to measure a degree of video quality according to the variation of scalability, it will play an important role at the extraction point for determining the quality of SVC.