This paper presents a novel rate-distortion (R-D) model for rate control of video coding. First, we investigate the well-known heat conduction equation (HCE) in the heat conduction process for a thin bar. HCE describes the dynamic distribution of temperature in a thin bar using a partial differential equation (PDE). Motivated by the heat conduction process in a thin bar, a new rate transmission equation (RTE) is proposed to describe the dynamic behaviors of the bit-rate fluctuation during video coding. Second, a particular solution of RTE is employed as our proposed R-D model, which consists of two variables, i.e., the distortion D and the source statistical character M. Based on our model, a corresponding rate-control scheme is proposed for MPEG coding. Finally, extensive experimental results are reported to show that, compared with the well-known MPEG-4 Q2 rate-control scheme, our proposed work achieves better buffer status, higher control accuracy, and stabler and more consistent picture quality.

Several scene-detection algorithms, which are only based on bit rate fluctuations, have been proposed. All of them are presented on the fixed thresholds, which are obtained by the empirical records of the video characteristics. Due to the sensitivity of these methods to the accuracy of the records, which are generally obtained by testing several values repeatedly, bad performance evaluation might be observed for the actual scene detection, especially for real-time video traffic. In this paper, we review the previous works in this area, and study the correlation between the scene duration and the scene change at the frame level, and simultaneously investigate the local statistical characteristics of scenes such as variance and peak bit rate etc. Based on this analysis, an effective decision function is first constructed for the scene segmentation. Then, we propose a scene-detection algorithm using the defined dynamic threshold model, which can capture the statistical properties of the scene changes. Experimental results using 15 variable bit rate MPEG video traces indicate good performances of the proposed algorithm with significantly improved scene-detection accuracy.

Burstiness property plays an important role in the variable bit rate (VBR) video traffic. One of the most significant issues in providing quality-of-service (QoS) guarantees for the real-time multimedia communications in a high-speed network is to estimate the burstiness accurately. In this paper, an efficient burstiness estimation model (fractal exponent model) for VBR video stream is proposed. In order to improve the accuracy of the burstiness measure, a new operator is firstly introduced, which is similar to the convolution operator. Then, using burstiness index derived from the fractal exponent function, we can estimate the burstiness characteristics of VBR video correctly. Simulation results using 20 VBR MPEG video traces indicate good performance of the proposed algorithm with significantly improved burstiness estimation accuracy.

In this paper, we presented a novel wavelet packet image coding approach which provides the functionality of fine granular bitstream scalability. The proposed progressive wavelet packet image coding scheme consists of three parts, wavelet packet decomposition, a quadtree sorting procedure for classifying wavelet coefficients and universal trellis-coded quantization for quantizing the sorted coefficients. The image coding results, calculated in PSNR and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results, due to the flexible representation ability of wavelet packet, the effective quadtree classifier and the improved granular fidelity of the UTCQ over scalar quantization.

In order to guarantee quality of service (QoS) over Internet, traffic analysis, traffic management have been active research areas. A lot of facts show the Internet traffic and variable bit rate videos streaming all are characterized by self-similar property. Hurst parameter as an important factor that reflects the self-similar property is a key to traffic management and QoS. In this paper existing wavelet methods for the estimation of the Hurst parameter of self-similar traffic is systematically analyzed and examined. The effects of wavelet functions, vanishing moments and wavelet decomposition levels to the results of wavelet methods for acquiring the Hurst parameter are investigated via numerical experiments. Some useful conclusions are drawn on the relationship between the accuracy of the methods and the selection of the order of vanishing moments and the selection of wavelet functions.

In the field of content-based visual information analysis, the detection of visual features is a significant topic. In order to process video data efficiently, visual features extraction is required. Many advanced video applications require direct manipulation of compressed video data. An effective approach that detects edges in MPEG compressed images is proposed. First, DCT coefficients of 8/spl times/8 subblocks are analyzed on their meaning in determining boundaries. Then, we consider two types of ideal linear edges cutting through a block of size 8/spl times/8. Based on these edge models, we derive an edge detection approach from ten normalized DCT coefficients, obtaining the general rules of edge classification. Finally, we test the proposed algorithm on different images, and compare our method with other edge classification approaches. Simulations show that our approach can be used to estimate the edge information of images from their DCT coefficients more effectively than those proposed previously.

A new method of nonseparable nonlinear wavelet decomposition is proposed, which is suited for the task of image compression, especially for lossless coding applications. It is based on a certain statistical operator that is defined here according to the Markov random field theory. In contrast to the previous nonlinear predictors such as the median or morphological operators, this statistical operator can sufficiently take advantage of the statistical correlation between neighboring pixels. It can be used to realize integer-valued wavelet transforms, which can avoid quantization with the image detail signals being zero (or almost zero) in the smooth gray-level variation areas at a big probability. Numerical results show that the entropy of the coefficients in the transform domain obtained with this new method is smaller than that obtained with the other nonlinear transform methods.

With the development of the Internet, variable bit rate (VBR) video will be the major component of future multimedia services. In order to guarantee quality of service (QoS) in real-time transmission, on-line prediction of VBR video traffic integrated with a mechanism for dynamic resource allocation in RCBR (renegotiate constant bit rate) networks has been an active research area. We exploit the short-range dependence (SRD) and long-range dependence (LRD) characteristics of MPEG video and develop a novel structure-based LMS algorithm to predict the bandwidth required by the future frame and group of pictures (GOP). Compared to the LMS algorithm, the modified LMS algorithm can predict the bit rate of frames quickly and accurately without any delay. Through analysis of the queuing of video traffic in the network buffer, dynamic bandwidth allocation using prediction in RCBR networks exhibits better performance than fixed bandwidth allocation.

To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long-range dependence (LRD). Its burstiness and self-similarity make it hard to control and guarantee QoS, so analysis of its queuing behavior attracts much interest and attention. A new scene based M/G/1 queuing model is put forward to analyze the queuing behavior of video streams in the network buffer. Integrated with the scene length distribution, the variance of scene size and the average bit rate of GOPs, the proposed model can accurately describe the queuing behavior of video traffic overall.