Abstract

A novel scalable video adaptation framework is developed to achieve the aim of ensuring enhanced Rate-Distortion (R-D) performances for the ubiquitous multimedia communication services in this study. This novel framework also integrates novel contributions in its development process. As the first contribution, novel models evaluating the motion information and spatial structure characteristics of the video contents are proposed. As the second novel contribution of the developed framework, adaptation experiments are conducted by exploiting a proposed adaptation approach relying on prioritizing the spatial, temporal, and quality layer based Network Abstraction Layer Units (NALUs) of the scalability extension of the H.264/Advanced Video Coding (AVC) gradually in accordance with the video contents’ motion and structural characteristics. The proposed adaptation approach also relies on the prioritization of the quality layer based NALUs from the key and non-key frames in both symmetric and asymmetric manners while attaining the target bit rate of a communication network. In accordance with these development steps, subjective experiments are carried to assess the quality of adapted video contents having different motion information and spatial structure characteristics under different ambient illumination conditions. Using the analysis results of the subjective experiments, another significant novelty of the framework relying on developing novel prioritization schemes, which assist an encoder to assign priority indices for each NALU of the encoded video contents based on their characteristics and the ambient illumination condition of the viewing environment, is performed. The adaptation experiments conducted in the light of the developed framework prove that highly efficient R-D performances can be attained for the video contents having any characteristic and viewed under any ambient illumination condition for assisting the multimedia communication services to ensure improved user viewing perception while easing the adverse effects of the network congestion.

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