Lisimachos P. Kondi

MPEG-4 and rate-distortion-based shape-coding techniques

We address the problem of the efficient encoding of object boundaries. This problem is becoming increasingly important in applications such as content-based storage and retrieval, studio and television postproduction, and mobile multimedia applications. The MPEG-4 visual standard will allow the transmission of arbitrarily shaped video objects. The techniques developed for shape coding within the MPEG-4 standardization effort are described and compared first. A framework for the representation of shapes using their contours is presented next. Such representations are achieved using curves of various orders, and they are optimal in the rate-distortion sense. Finally, conclusions are drawn.

Joint source-channel coding for motion-compensated DCT-based SNR scalable video

In this paper, we develop an approach toward joint source-channel coding for motion-compensated DCT-based scalable video coding and transmission. A framework for the optimal selection of the source and channel coding rates over all scalable layers is presented such that the overall distortion is minimized. The algorithm utilizes universal rate distortion characteristics which are obtained experimentally and show the sensitivity of the source encoder and decoder to channel errors. The proposed algorithm allocates the available bit rate between scalable layers and, within each layer, between source and channel coding. We present the results of this rate allocation algorithm for video transmission over a wireless channel using the H.263 Version 2 signal-to-noise ratio (SNR) scalable codec for source coding and rate-compatible punctured convolutional (RCPC) codes for channel coding. We discuss the performance of the algorithm with respect to the channel conditions, coding methodologies, layer rates, and number of layers.

An image super-resolution algorithm for different error levels per frame

In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.

Error concealment algorithms for compressed video

Abstract In this paper we propose an error concealment algorithm for compressed video sequences. For packetization and transmission, a two layer ATM is utilized so that the location of information loss is easily detected. The coded image can be degraded due to channel error, network congestion, and switching system problems. Seriously degraded images may therefore result due to information loss represented by DCT coefficients and motion vectors, and due to the inter-dependency of information in predictive coding. In order to solve the error concealment problem of intra frames, two spatially adaptive algorithms are introduced; an iterative and a recursive one. We analyze the necessity of an oriented high pass operator we introduce, and the requirement of changing the initial condition in iterative regularized recovery algorithm. Also, the convergence of iteration is analyzed. In recursive interpolation algorithm, the edge direction of the missing areas is estimated from the neighbors, and estimated edge direction is utilized for steering the direction of interpolation. For recovery of the lost motion vectors, an overlapped region matching algorithm is introduced. Several experimental results are presented.

Maximizing user utility in video streaming applications

We study some of the design tradeoffs of video streaming systems in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the quality of the received video sequence. We also consider the cost to the network user for streaming the video sequence. We have formulated this utility maximization problem as a joint constrained optimization problem where we maximize the difference between the utility and the network cost, subject to the constraint that the decoder buffer does not underflow. In this manner, we can find the optimal tradeoff between video quality and network cost. We present a deterministic dynamic programming approach for both the constant bit rate and renegotiated constant bit rate service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.

4G Wireless Video Communications

A comprehensive presentation of the video communication techniques and systems, this book examines 4G wireless systems which are set to revolutionise ubiquitous multimedia communication.4G Wireless Video Communications covers the fundamental theory and looks at systems descriptions with a focus on digital video. It addresses the key topics associated with multimedia communication on 4G networks, including advanced video coding standards, error resilience and error concealment techniques, as well as advanced content-analysis and adaptation techniques for video communications, cross-layer design and optimization frameworks and methods. It also provides a high-level overview of the digital video compression standard MPEG-4 AVC/H.264 that is expected to play a key role in 4G networks. Material is presented logically allowing readers to turn directly to specific points of interest. The first half of the book covers fundamental theory and systems, while the second half moves onto advanced techniques and applications. This book is a timely reflection of the latest advances in video communications for 4G wireless systems. One of the first books to study the latest video communications developments for emerging 4G wireless systems Considers challenges and techniques in video delivery over 4G wireless systems Examines system architecture, key techniques and related standards of advanced wireless multimedia applications Written from both the perspective of industry and academia

Construction of Incoherent Unit Norm Tight Frames With Application to Compressed Sensing

Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight frames (ETFs), their construction has been proven extremely difficult. The few known techniques produce only a small number of such frames while imposing certain restrictions on frame dimensions. Motivated by the application of incoherent tight frames in compressed sensing (CS), we propose a methodology to construct incoherent UNTFs. When frame redundancy is not very high, the achieved maximal column correlation becomes close to the lowest possible bound. The proposed methodology may construct frames of any dimensions. The obtained frames are employed in CS to produce optimized projection matrices. Experimental results show that the proposed optimization technique improves CS signal recovery, increasing the reconstruction accuracy. Considering that the UNTFs and ETFs are important in sparse representations, channel coding, and communications, we expect that the proposed construction will be useful in other applications, besides the CS.

MAP based resolution enhancement of video sequences using a Huber-Markov random field image prior model

In this paper, we propose two approaches for video sequence resolution enhancement using maximum a posteriori (MAP) estimation. Huber-Markov random fields (HMRF) are used as prior models. These models can better preserve image discontinuities (edges) when compared with Gaussian prior models. The two proposed approaches differ in the selection of image smoothness measure. The first approach employs a measure that is based on a discrete Laplacian kernel, while the second approach uses a finite difference approximation of second order derivatives at each pixel of the high-resolution image estimate. Experimental results are presented and conclusions are drawn.

A regularization framework for joint blur estimation and super-resolution of video sequences

In traditional digital image restoration, the blurring process of the optic is assumed known. Many previous research efforts have been trying to reconstruct the degraded image or video sequence with either partially known or totally unknown point spread function (PSF) of the optical lens, which is commonly called the blind deconvolution problem. Many methods have been proposed in the application to image restoration. However, little work has been done in the super-resolution scenario. In this paper, we propose a generalized framework of regularized image/video iterative blind deconvolution / super-resolution (IBD-SR) algorithm, using some information from the more matured blind deconvolution techniques from image restoration. The initial estimates for the image restoration help the iterative image/video super-resolution algorithm converge faster and be stable. Experimental results are presented and conclusions are drawn.

Joint source-channel coding for scalable video using models of rate-distortion functions

A joint source-channel coding scheme for scalable video is developed in this paper. An SNR scalable video coder is used and unequal error protection (UEP) is allowed for each scalable layer. Our problem is to allocate the available bit rate across scalable layers and, within each layer, between source and channel coding, while minimizing the end-to-end distortion of the received video sequence. The resulting optimization algorithm we propose utilizes universal rate-distortion characteristic plots. These plots show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate (the error rate that remains after the use of channel coding). Models for these plots are proposed in order to reduce the computational complexity of the solution. Experimental results demonstrate the effectiveness of the proposed approach.