Wei Liu

H.264/SVC parameter optimization based on quantization parameter, MGS fragmentation, and user bandwidth distribution

In the situation of limited bandwidth, how to improve the performance of scalable video coding plays an important role in video coding. The previously proposed scalable video coding optimization schemes concentrate on reducing coding computation or trying to achieve consistent video quality; however, the connections between coding scheme, transmission environments, and users’ accesses manner were not jointly considered. This article proposes a H.264/SVC (scalable video codec) parameter optimization scheme, which attempt to make full use of limited bandwidth, to achieve better peak signal-to-noise ratio, based on the joint measure of user bandwidth range and probability density distribution. This algorithm constructs a relationship map which consists of the bandwidth range of multiple users and the quantified quality increments measure, QPe, in order to make effective use of the video coding bit-stream. A medium grain scalability fragmentation optimization algorithm is also presented with respect to user bandwidth probability density distribution, encoding bit rate, and scalability. Experiments on a public dataset show that this method provides significant average quality improvement for streaming video applications.

An image super resolution reconstruction algorithm based on Undecimated Morphological Wavelet

The paper proposes an image super resolution reconstruction (SRR) algorithm based on Undecimated Morphological Wavelet (UMW). As UMW can maintain more gradient information and eliminate more artificial blocks, it is used to decompose low resolution (LR) images. High frequency coefficients from the decomposition contain the gradients that reflect the horizontal, vertical, and diagonal domain information. Then both the low frequency and high frequency coefficients are interpolated to produce an extended group of coefficient sequence. Finally, the extended coefficients are fused and using inverse transformation to produce a super resolution image. Experimental results show that that the proposed algorithm have good performance in the image reconstruction quality and the computational efficiency.

A novel simple candidate set method for symmetric TSP and its application in MAX-MIN ant system

Traveling Salesman Problem (TSP) is a kind of typical NP problem and has been extensively researched in combinatorial optimization. For solving it more effectively, candidate set is used in many algorithms in order to limit the selecting range when choosing next city to move, such as in Ant Systems, or to initialize a local optimum solution, such as in Lin-Kernighan Heuristic (LKH) algorithm. A novel simple method for generating candidate set is proposed in this paper and applied into MAX-MIN Ant System (MMAS) for symmetric TSP problem. Experimental results show that it has better performance than other Ant Systems including MMAS. Moreover, this method can be used in other algorithms for symmetric TSP problem.

A video adaptation framework for SVC based on content description

Due to todays diverse resource constraints and user preferences, video adaptation attracts more and more research interests. A video adaptation framework for scalable video coding(SVC) is proposed in this paper. The content description is acquired off-line, and then a bitstream syntax description is automatic created by a format-specific parser. Finally, video adaptation is implemented based on bitstream analysis and video content description. Experiments show that the proposed framework can acquire output video stream quickly and assure visual quality without loss of coding efficiency.

Progressive Description of Video Streaming Media Based on Subjective Perception

Video description is a critical component in video streaming media processing and transmission. Different video data have different levels of contribution to the overall video presentation quality at the receiver side. on the premise of related work, video scene classification, visual sensitive regions(VSR) extraction and MGS(Medium Grain Scalability) encoding, a new approach to progressive description of Video Streaming Media(VSM) based on subjective perception is proposed in this paper. Frames importance according to coding key parameters and slice data importance are determined Firstly. Then VSR and important data protection are jointly considered, a new approach to progressive description of VSM adapted to network bandwidth and quality scalability is proposed. Applying MGS encoding, experimental study is performed on Mobile IP testing platform, the experimental results demonstrate that the proposed approach to progressive description of VSM could effectively protect video important data and significantly improve the video presentation quality.

Test Conditions and Error Concealment for SVC Based on RTP Encapsulation

Real-time Transport Protocol (RTP) is usually used to provide real time transportation and quality guarantee of scalable video coding (SVC) stream. Base on the analysis of SVC stream structure and real-time transport protocol, RTP encapsulation is implemented for H.264/SVC stream. A simulation method is proposed to simulate the transport process of the scalable video in the common test conditions by isolating the base layer and enhancement layers. Error concealment based on RTP encapsulation is proposed to improve the video quality, and it solve the problem that enhancement layer may be lost. Simulation test shows the effectiveness, compatibility and extendibility of the RTP encapsulation mechanism.

Error Control for MGS-Based SVC Bitstream

Error Control is an important aspect for video coding and transmission. In this paper, we study error control for MGS-based bitstream. Our method not only duplicate the key data of MGS slice to improve the quality of decoded video, but also construct a compensated MGS slice to maintain bitstream conformance. Experiments show better effect and better performance of the proposed method compared to the conventional method of SVC.