Hayder Radha

The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP

Real-time streaming of audiovisual content over the Internet is emerging as an important technology area in multimedia communications. Due to the wide variation of available bandwidth over Internet sessions, there is a need for scalable video coding methods and (corresponding) flexible streaming approaches that are capable of adapting to changing network conditions in real time. In this paper, we describe a new scalable video-coding framework that has been adopted recently by the MPEG-4 video standard. This new MPEG-4 video approach, which is known as Fine-Granular-Scalability (FGS), consists of a rich set of video coding tools that support quality (i.e., SNR), temporal, and hybrid temporal-SNR scalabilities. Moreover, one of the desired features of the MPEG-J FGS method is its simplicity and flexibility in supporting unicast and multicast streaming applications over IF.

A hybrid temporal-SNR fine-granular scalability for Internet video

Transmission of video over bandwidth varying networks like the Internet requires a highly scalable solution capable of adapting to the network condition in real-time. To address this requirement, scalable video-coding schemes with multiple enhancement layers have been proposed. However, under this multiple-layer paradigm, the transmission bit-rate of each layer has to be predetermined at encoding time. Consequently, the range of bit-rates that can be covered with these compression schemes is limited and often lower than, or different from, the desired range required at transmission time. In this paper, a novel scalable video-coding framework and a corresponding compression method for Internet video streaming are introduced. Building upon the MPEG-4 SNR fine-granular-scalability (FGS) approach, the proposed framework provides a new level of abstraction between the encoding and transmission process by supporting both SNR and temporal scalability through a single enhancement layer. Therefore, our proposed approach enables streaming systems to support full SNR, full temporal, and hybrid temporal-SNR scalability in real-time depending on the available bandwidth, packet-loss patterns, user preferences, and/or receiver complexity. Moreover, our experiments revealed that the presented FGS temporal-SNR scalability has similar or better PSNR performance than the multilayer scalability schemes. Subsequently, an Internet video streaming system employing the proposed hybrid FGS-temporal scalability structure is introduced, together with a very simple, yet effective, rate-control that performs the tradeoffs between individual image quality (SNR) and motion-smoothness in real-time. The hybrid temporal-SNR scalability presented in this paper has been adopted in the MPEG-4 standard to support video-streaming applications.

Translation-Invariant Contourlet Transform and Its Application to Image Denoising

Most subsampled filter banks lack the feature of translation invariance, which is an important characteristic in denoising applications. In this paper, we study and develop new methods to convert a general multichannel, multidimensional filter bank to a corresponding translation-invariant (TI) framework. In particular, we propose a generalized algorithme agrave trous, which is an extension of the algorithme agrave trous introduced for 1-D wavelet transforms. Using the proposed algorithm, as well as incorporating modified versions of directional filter banks, we construct the TI contourlet transform (TICT). To reduce the high redundancy and complexity of the TICT, we also introduce semi-translation-invariant contourlet transform (STICT). Then, we employ an adapted bivariate shrinkage scheme to the STICT to achieve an efficient image denoising approach. Our experimental results demonstrate the benefits and potential of the proposed denoising approach. Complexity analysis and efficient realization of the proposed TI schemes are also presented

Wavelet-based contourlet transform and its application to image coding

In this paper, we first propose a new family of geometrical image transforms that decompose images both radially and angularly. Our construction comprises two stages of filter banks that are non-redundant and perfect reconstruction and therefore lead to an overall non-redundant and perfect reconstruction transform. Using the wavelet transform as the first stage, we apply directional filter banks to the wavelet coefficients in such a way to maintain the anisotropy scaling law. Furthermore, we propose a new image coding scheme based on the proposed transform, the wavelet-based contourlet transform (WBCT), using a new contourlet-based set partitioning in hierarchical trees (CSPIHT) algorithm that provides an embedded code. Due to differences in parent-child relationships between the WBCT coefficients and wavelet coefficients, under CSPIHT, we developed an elaborated repositioning algorithm for the WBCT coefficients in such a way that we could scan spatial orientation trees that are similar to the original SPIHT algorithm. Our experiments demonstrate that the proposed approach is efficient in coding images that possess mostly textures and contours. Our simulation results also show that this new coding approach is competitive to the wavelet coder in terms of the PSNR-rate curves, and is visually superior to the wavelet coder for the mentioned images.

Unequal packet loss resilience for fine-granular-scalability video

Several embedded video coding schemes have been recently developed for multimedia streaming over IP. In particular, fine-granular-scalability (FGS) video coding has been recently adopted by the MPEG-4 standard as the core video-compression method for streaming applications. From its inception, the FGS scalability structure was designed to be packet-loss resilient especially under unequal packet-loss protection (UPP). However, since the introduction of FGS, there has not been a comprehensive study evaluating its packet-loss resilience under unrecoverable packet losses that are common in Internet streaming applications. In this paper, we evaluate two important aspects of FGS packet-loss resilience. First, we study the impact of applying UPP between the base- and enhancement-layers on FGS-based streams, and we compare equal packet-loss protection (EPP) with UPP scenarios. Second, we introduce the notion of fine-grained loss protection (FGLP), which is suitable for the FGS enhancement-layer, and we develop an analytical framework for evaluating FGLP bounds. Based on these bounds, we show the impact of applying fine-grained protection to the FGS enhancement-layer for different types of video sequences and over a wide range of bit-rates and packet-loss ratios. As illustrated by our extensive simulation results, applying 1) UPP between the base- and enhancement-layers and 2) FGLP for the FGS enhancement-layer can provide significant resilience under moderate-to-high packet-loss ratios (e.g., 5-10%). Furthermore, the merits of this new packet-loss protection technique go beyond the FGS coding scheme, because FGLP can be successfully applied to improve the resilience to packet-losses of other embedded video coding techniques.

Measurement study of low-bitrate internet video streaming

In this paper, we analyse the results of a seven-month real-time streaming experiment, which was conducted between a number of unicast dialup clients, connecting to the Internet through access points in more than 600 major U.S. cities, and a backbone video server. During the experiment, the clients streamed low-bitrate MPEG-4 video sequences from the server over paths with more than 5,000 distinct Internet routers. We describe the methodology of the experiment, the architecture of our NACK-based streaming application, study end-to-end dynamics of 16 thousand ten-minute sessions (85 million packets), and analyze the behavior of the following network parameters: packet loss, round-trip delay, one-way delay jitter, packet reordering, and path asymmetry. We also study the impact of these parameters on the quality of real-time streaming.

The contourlet transform for image denoising using cycle spinning

A new method for image denoising based on the contourlet transform, which has been recently introduced is presented in this paper. Image denoising by means of the contourlet transform introduces many visual artifacts due to the Gibbs-Iike phenomena. Due to the lack of translation invariance of the contourlet transform, we employ a cycle-spinning-based technique to develop translation invariant contourlet denoising scheme. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. Our experiments show that the proposed approach outperforms the translation invariant wavelets both visually and in terms of the PSNR values, especially for the images that include mostly fine textures and contours.

Markov-based modeling of wireless local area networks

Errors introduced by a wireless medium are more frequent and profound than contemporary wired media. Some of these errors, which are not corrected by the physical layer, result in Medium Access Control (MAC) layer bit errors and packet losses. Design of wireless protocols and applications can benefit substantially from a thorough understanding of these MAC layer impairments. This paper evaluates and proposes Markov-based stochastic chains to model the 802.11b MAC-to-MAC channel behavior for both bit errors and packet losses. We introduce an Entropy Normalized Kullback-Leibler measure to evaluate the performance of existing and new bit error and packet loss models. Based on the proposed measure, and contrary to recent results for mobile networks, we demonstrate that the traditional two-state Markov chain provides a very suitable model for the 802.11b MAC-to-MAC packet loss process. However, this simple model is not adequate for bit errors observed at the MAC layer of wireless local area networks. Consequently, we evaluate three other Markov-based chains for modeling these errors: full-state, hidden, and hierarchical Markov chains. Among these chains, we illustrate that the full-state Markov bit error model, evaluated under a wide range of order values, renders the best performance.

A New Family of Nonredundant Transforms Using Hybrid Wavelets and Directional Filter Banks

We propose a new family of nonredundant geometrical image transforms that are based on wavelets and directional filter banks. We convert the wavelet basis functions in the finest scales to a flexible and rich set of directional basis elements by employing directional filter banks, where we form a nonredundant transform family, which exhibits both directional and nondirectional basis functions. We demonstrate the potential of the proposed transforms using nonlinear approximation. In addition, we employ the proposed family in two key image processing applications, image coding and denoising, and show its efficiency for these applications

Image compression using binary space partitioning trees

For low bit-rate compression applications, segmentation-based coding methods provide, in general, high compression ratios when compared with traditional (e.g., transform and subband) coding approaches. In this paper, we present a new segmentation-based image coding method that divides the desired image using binary space partitioning (BSP). The BSP approach partitions the desired image recursively by arbitrarily oriented lines in a hierarchical manner. This recursive partitioning generates a binary tree, which is referred to as the BSP-tree representation of the desired image. The most critical aspect of the BSP-tree method is the criterion used to select the partitioning lines of the BSP tree representation, In previous works, we developed novel methods for selecting the BSP-tree lines, and showed that the BSP approach provides efficient segmentation of images. In this paper, we describe a hierarchical approach for coding the partitioning lines of the BSP-tree representation. We also show that the image signal within the different regions (resulting from the recursive partitioning) can be represented using low-order polynomials. Furthermore, we employ an optimum pruning algorithm to minimize the bit rate of the BSP tree representation (for a given budget constraint) while minimizing distortion. Simulation results and comparisons with other compression methods are also presented.