Video Adaptive Technology Research, Based On The Video Signal As Described

Internet based multimedia application is the evolution trend of multimedia techniques. Current complex media environment provides users flexible media data access, which improves working efficiency and life quality of users. However, the diversity property also brings serious difficulties in media data access. Video adaptation is the most promising solution for achieving universal media access.We studied video adaptation techniques in this dissertation. Firstly, we analyzed existing video adaptation framework and found that it is not appropriate for designing general and efficient video adaptation operations. Based on this analysis, we proposed a novel video adaptation framework and some related algorithms. The main contributions of this dissertation can be summarized as follows.â‘ We proposed a video adaptation framework based on video signal representation, in which the key point is how to describe video signal to facilitate it in video adaptation operations. We also proposed the set of feature descriptors as the representation of video signal. Each descriptor reflects some characteristics of original video signal. By making using of these descriptors, we can develop simple and efficient video adaptation operations.â‘¡Based on the framework above, we proposed a motion description algorithm for bit-rate adaptation. Firstly, we proposed a mode-based hierarchical model for motion information. Secondly, we proposed a pre-encoding method to achieve motion descriptor. We also developed a method of motion extraction. The generated motion descriptor can be applied in fast bit stream generation. While encoding a video in some bit-rate, the encoder can extract proper motion information to accelerate the encoding process. Due to skipping motion estimation, the encoding complexity can be reduced greatly.â‘¢We proposed an algorithm to extract rate-distortion information from original video signal. The information can be used in bit allocation. We analyzed the dependency among predictive frames and proposed a linear model for it. We also introduced the definition of impact factor. Based on the linear model, we proposed a method to modify the rate-distortion function of each frame. Then, the modified functions with dependency consideration reflect the relationships between the whole distortion and the single frame rate. With these modified functions, it is easy to achieve optimal bit allocation to improve the encoding performance.â‘£We proposed a spatial adaptation framework based on attention information. The limited display size of mobile devices hinders the perceptive experiences of users when they browse high-resolution videos. Based on the idea of ROI, we proposed to transform high-resolution videos into low-resolution ones which are composed of attention areas in each frame, which will meet the constraint from the limited display size. At the same time, most of attention information in original video signal will be remained. With this framework, the perceptive experience of mobile users will be improved. The whole framework includes three algorithms: the extraction and encapsulation of attention information, QP adaptive adjustment based on attention information, and fast mode decision for transcoding.In conclusion, we studied video adaptation techniques and achieved some valuable results. Till now, the proposed framework is still at the initial stage and there exist many problems needed to be solved. The video adaptation framework based on video signal representation is a potential research field which is worthy of further study.