Workload model for video decoding and its applications

Abstract

In this thesis, we study the relationship between decoding workload and video quality. Based on the analysis of video structure and decoder implementations, we propose a decoding workload model. Given a video clip, the model can accurately estimate the decoding workload on the target platform with very low computational complexity. Experiments are conducted to test the robustness of the model. The experiment results show that the model is generic to different decoder implementations and target platforms. We also propose two relevant video applications: the decoding workload scalable transcoder and the decoding workload scalable encoder. Based on the decoding workload model, the proposed transcoder / encoder is able to generate a video clip which matches the decoding workload of the client while striving to achieve the best video quality. The transcoder /encoder can also balance the tradeoff between frame rate and individual frame quality.