Hongxing Guo

Rate allocation for transform domain Wyner-Ziv video coding without feedback

In this paper, we propose a new rate allocation algorithm for transform domain Wyner-Ziv video coding (WZVC) without feedback. In contrast to conventional video coding, Wyner-Ziv video coding aims to design simple intra-frame encoding and complex inter-frame decoding based on the Slepian-Wolf and Wyner-Ziv distributed source coding theorems. To allocate proper number of bits to each frame, most existing Wyner-Ziv video coding solutions need a feedback channel (FC) at the decoder. However, in many video coding applications, the FC is not allowed. Moreover, the FC will introduce latency and an increase of decoder complexity because several iterative decoding operations may be needed to decode the data to achieve target video quality. The proposed algorithm predicts the number of bits for each Wyner-Ziv frame at the encoder as a function of the coding mode and the quantization parameters. Such predictions will not significantly increase the complexity at the encoder. However, the prediction will be able to properly select the best mode and quantization parameter for encoding each Wyner-Ziv frame. Experimental results show that the proposed algorithms is able to achieve good encoder rate allocation while still maintains consistent coding efficiency. Comparing to the WZVC coder with FC, this new WZVC coder without FC induces only a small loss in Rate-Distortion performance.

Feedback-free rate-allocation scheme for transform domain Wyner---Ziv video coding

In most existing Wyner–Ziv video coding schemes, a feedback channel (FC) is expected at the decoder in order to allocate a proper bit rate for each Wyner–Ziv frame. However, FC not only results in additional latency but also increases decoding complexity due to the several feedback-decoding iterations. Moreover, FC may be unavailable in many practical video applications. In this paper, we propose a novel feedback-free rate-allocation scheme for transform domain Wyner–Ziv video coding (TD-WZVC), which predicts the rate for each Wyner–Ziv frame at the encoder without significantly increasing the complexity of the encoder. First, a correlation estimation model is presented to characterize the relationship between the source frame and the reference frame estimated at the encoder in TD-WZVC. Then, an efficient FC-free rate-allocation algorithm is proposed and a linear model is built to avoid both overestimation and underestimation of the real rate and obtain an optimal rate-distortion performance. Experimental results show that the proposed scheme is able to achieve a good encoder rate allocation while still maintaining consistent coding efficiency.

A robust foreground segmentation method by temporal averaging multiple video frames

Foreground segmentation in videos by background subtraction methods are widely used in video surveillance applications. Adaptive single or mixture Gaussian models have been adopted for modeling nonstationary temporal distributions of background pixels. However, a challenge for this approach is that it is hard to choose a threshold to separate foreground from background accurately because of the so-called camouflage problem. This paper proposes a simple and effective scheme to alleviate the problem. It is achieved by averaging the frames in video sequences temporally, which reduces the variances of background models. Thus the background model is squeezed to a very narrow region and the probability of camouflage is reduced dramatically, which helps to improve the sensitivity and reliability. Significant improvements are shown on real video data. Incorporating this algorithm into a statistical framework for background subtraction leads to an improved foreground segmentation performance compared to a standard method.

A New Approach for the Shortest Path Problem with Vague Sets

The greatly studies show that it is quite appropriate to use fuzzy theory to solve the shortest path problem. This paper analyses the general method of dealing with the shortest path problem by using discrete fuzzy arc length, and points out the issue of such methods. As carrying on various kinds of operation between the fuzzy numbers and sets, some information will be lost. In this paper, some related vague sets operations and vague similarity measure are presented, and a new approach is developed to solve the shortest path problem in network base on vague sets. The discrete vague shortest length method is proposed to find the vague shortest length, and the vague similarity measure is utilized to obtain the shortest path. At last, an illustrative example is given to demonstrate that the result of vague sets method is closer to intuitive judgment than fuzzy sets method.

Energy-efficient co-scheduling of receiving packets and decoding tasks on mobile video streaming terminals

An energy-efficient co-scheduling policy for packet receiving and video decoding tasks is proposed for a mobile terminal receiving video streaming packets from a access point(AP) in a WLAN. Through analyzing the interaction between the network interface card(NIC) and the CPU, a frame buffer between the NIC and the CPU is set to facilitate the cooperation of video packet receiving and video decoding tasks. Based on monitoring the fullness of the frame buffer, the NIC can dynamically adjust its operating mode so as to receive data packets in bursts at the optimal wireless channel state; accordingly, the CPU can also dynamically adjust its operating frequency as lowly as possible to reduce the energy consumption while meeting the deadline of a video frame. This co-scheduling scheme makes full use of the cooperativity between video packet receiving and decoding tasks in a dynamic environment where the computational complexity of the video decoding varies with the video scene changing and wireless network communication bandwidth also varies with time. The experimental results show that it can achieve 30%-35% energy saving for video streaming applications.

On the Shortest Path to Solve the Problem Based on Vague Sets

The shortest path problem (SPP) is currently being greatly studied in fuzzy sets and systems area. Previously published algorithms and methods for the fuzzy shortest path problem based on discrete or continuous types fuzzy sets of fuzzy arc lengths. However, to present an arc real length in vague set is more reasonable than in fuzzy set. Moreover,carrying on various kinds of operation between the fuzzy numbers and sets, we find that some information will be lost.In this paper , we present a novel approach to solve the shortest path problem in network base on vague sets. Firstly, we determine the vague shortest path length from source node to the destination node in the classical directed network. Secondly, by mean of vague similarity measure to evaluate similarity degree between vague path lengths. At last ,Comparing the result of similarity measure,the path with the highest similarity degree is the shortest path. An illustrative example is given to demonstrate that the result of the approach with vague sets is closer to intuitive judgment.

An memory-efficient variable length decoding scheme for embedded MPEG-4 video decoders

Variable length decoding (VLD) is inherent bit-serial operation and is the first stage of the whole video decoding task, the overall decoding system performance is determined by its throughput and efficiency. In this paper, a scheme for decoding of discrete cosine transform (DCT) coefficients is presented The DCT VLD tables are split into two different tables: the one with 0-3 leading zeros and the other with 4 or more leading zeros. The proposed VLD tables need less than 6K bytes memory space, and the decoding procedures are completed by applying numerical properties to codewords identification and symbol indexing. Compared with the direct indexing method, not only the memory space is reduced for symbol information, but also the code efficiency is improved by enabling compiling code with near mode. Experimental results show that the proposed solution can improve the VLD speed by 17-43%. This VLD method has been integrated into the developed embedded MPEG-4 video decoder successfully

Linear model-based adaptive prediction for video decoding complexity

This paper proposes a novel approach to predict the video decoding computational complexity. The decoding complexity of each frame is found having an approximate linear relationship with the frame length, whereas the motion Information (MI) and the amount of encoded residual coefficients (ERC) are proved to be the two main factors that affect the variation of the model parameters. The changing rule of MI and ERC for different kinds of video contents are investigated, which in turn derives the variation regularity of the model parameters. Then the correlation between the model parameters of neighboring frames is defined as a piecewise function under the constraint of video motion complexity. The derived piecewise function is used to predict the decoding complexity online, and the prediction error is utilized as the feedback to update the model parameters adaptively. Experimental results show that the proposed method can give fairly accurate prediction for the decoding complexity with very low overhead. The average prediction errors for various test sequences are all within 7%.

Understanding Indoor Scene: Spatial Layout Estimation, Scene Classification, and Object Detection

In this paper, we seek to understand scene from different viewpoints such as estimating the spatial layout of indoor scenes, detecting objects in the scene and making scene classification. In the previous work, every step has been done in a separate process so in this work we combine the three steps in one algorithm. To understand the indoor scene this requires interpreting the estimation of multiple scene elements and studying the relation between them. We propose a method that takes a single indoor image and then the relation between scene elements can be automatically learned and applied to totally understand the indoor scenes. Our proposed method consists of two steps: the first step is estimating the spatial layout of indoor scenes and this is done by extracting line segments and estimating the three orthogonal vanishing points. The orientation of the surfaces in the scene is defined by the vanishing points and so constructs the layout of the walls, ceiling, and floor. The second step is studying the relation between scene elements and this means that studying the relation between scene classification, objects in the scene, and layout estimation. We conduct extensive experiments to evaluate our proposed method performance on a single indoor image. We achieve superior accuracy results on two public datasets and provide a robust estimation of scene type, estimation of spatial layout and 3D objects.

Cross-Layer Transmission for Video Streaming in Wireless Relay Networks

In this paper, a novel cross-layer optimization scheme for video streaming in wireless relay networks is proposed. It takes into account of delay constraints at the application (APP) layer, relay node selection at the MAC layer and dynamic fading of wireless channels at the physical (PHY) layer in an integrated mode to improve the quality of video transmission. At the PHY layer, a finite state Markov channels (FSMC) model is used to estimate the channel states and a modified RTS/CTS scheme is proposed to facilitate the path selection at the MAC layer. The impacts of delay constraints and other factors at the APP layer are investigated. At the MAC layer, the source node can choose the proper path to transmit video data while considering video delay requirements and wireless channel states simultaneously. A typical three-node uploading scenario in video surveillance applications is considered as an example for performance evaluation. The experimental results show that the proposed scheme could improve video transmission quality and achieve a near-optimal performance.