Ashraf A. Kassim

A novel unrestricted center-biased diamond search algorithm for block motion estimation

The widespread use of block-based interframe motion estimation for video sequence compression in both MPEG and H.263 standards is due to its effectiveness and simplicity of implementation. Nevertheless, the high computational complexity of the full-search algorithm has motivated a host of suboptimal but faster search strategies. A popular example is the three-step search (TSS) algorithm. However, its uniformly spaced search pattern is not well matched to most real-world video sequences in which the motion vector distribution is nonuniformly biased toward the zero vector. Such an observation inspired the new three-step search (NTSS) which has a center-biased search pattern and supports a halfway-stop technique. It is faster on average, and gives better motion estimation as compared to the well-known TSS. Later, the four-step search (4SS) algorithm was introduced to reduce the average case from 21 to 19 search points, while maintaining a performance similar to NTSS in terms of motion compensation errors. We propose a novel unrestricted center-biased diamond search (UCBDS) algorithm which is more efficient, effective, and robust than the previous techniques. It has a best case scenario of only 13 search points and an average of 15.5 block matches. This makes UCBDS consistently faster than the other suboptimal block-matching techniques. This paper also compares the above methods in which both the processing speed and the accuracy of motion compensation are tested over a wide range of test video sequences.

Estimating Just-Noticeable Distortion for Video

Just-noticeable distortion (JND), which refers to the maximum distortion that the human visual system (HVS) cannot perceive, plays an important role in perceptual image and video processing. In comparison with JND estimation for images, estimation of the JND profile for video needs to take into account the temporal HVS properties in addition to the spatial properties. In this paper, we develop a spatio-temporal model estimating JND in the discrete cosine transform domain. The proposed model incorporates the spatio-temporal contrast sensitivity function, the influence of eye movements, luminance adaptation, and contrast masking to be more consistent with human perception. It is capable of yielding JNDs for both still images and video with significant motion. The experiments conducted in this study have demonstrated that the JND values estimated for video sequences with moving objects by the model are in line with the HVS perception. The accurate JND estimation of the video towards the actual visibility bounds can be translated into resource savings (e.g., for bandwidth/storage or computation) and performance improvement in video coding and other visual processing tasks (such as perceptual quality evaluation, visual signal restoration/enhancement, watermarking, authentication, and error protection)

Highly scalable wavelet-based video codec for very low bit-rate environment

We introduce a highly scalable video compression system for very low bit-rate videoconferencing and telephony applications around 10-30 kbits/s. The video codec first performs a motion-compensated three-dimensional (3-D) wavelet (packet) decomposition of a group of video frames, and then encodes the important wavelet coefficients using a new data structure called tri-zerotrees (TRI-ZTR). Together, the proposed video coding framework forms an extension of the original zero tree idea of Shapiro (1992) for still image compression. In addition, we also incorporate a high degree of video scalability into the codec by combining the layered/progressive coding strategy with the concept of embedded resolution block coding. With scalable algorithms, only one original compressed video bit stream is generated. Different subsets of the bit stream can then be selected at the decoder to support a multitude of display specifications such as bit rate, quality level, spatial resolution, frame rate, decoding hardware complexity, and end-to-end coding delay. The proposed video codec also allows precise bit rate control at both the encoder and decoder, and this can be achieved independently of the other video scaling parameters. Such a scheme is very useful for both constant and variable bit rate transmission over mobile communication channels, as well as video distribution over heterogeneous multicast networks. Finally, our simulations demonstrated comparable objective and subjective performance when compared to the ITU-T H.263 video coding standard, while providing both multirate and multiresolution video scalability.

Recognizing human group activities with localized causalities

The aim of this paper is to address the problem of recognizing human group activities in surveillance videos. This task has great potentials in practice, however was rarely studied due to the lack of benchmark database and the difficulties caused by large intra-class variations. Our contributions are two-fold. Firstly, we propose to encode the group-activities with three types of localized causalities, namely self-causality, pair-causality, and group-causality, which characterize the local interaction/reasoning relations within, between, and among motion trajectories of different humans respectively. Each type of causality is expressed as a specific digital filter, whose frequency responses then constitute the feature representation space. Finally, each video clip of certain group activity is encoded as a bag of localized causalities/filters. We also collect a human group-activity video database, which involves six popular group activity categories with about 80 video clips for each in average, captured in five different sessions with varying numbers of participants. Extensive experiments on this database based on our proposed features and different classifiers show the promising results on this challenging task.

Segmentation of volumetric MRA images by using capillary active contour

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) images can be a very useful computer aided diagnosis (CAD) tool for clinical routines. Level sets based evolution schemes, which have been shown to be effective and easy to implement for many segmentation applications, are being applied to MRA data sets. In this paper, we present a segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm models capillary action and derives a capillary active contour for segmentation of thin vessels. The algorithm is implemented using the level set method and has been applied successfully on real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, experiments show that our method facilitates more accurate segmentation of thin blood vessels.

Embedded color image coding using SPIHT with partially linked spatial orientation trees

This paper describes a variation of the set partitioning in hierarchical trees (SPIHT) scheme for color image coding. By using partially linked spatial orientation tree structures across different spectral planes, the new color-SPIHT scheme is able to embed both chrominance and luminance data in the coded bit stream. The performance is comparable to that of a SPIHT-based coding scheme but with significantly lower computational complexity.

Design and implementation of parallel video encoding strategies using divisible load analysis

The processing time needed for motion estimation usually accounts for a significant part of the overall processing time of the video encoder. To improve the video encoding speed, reducing the execution time for motion estimation process is essential. Parallel implementation of video encoding systems using either the software or the hardware approach has attracted much attention in the area of real time video coding. In this paper, we attempt to implement a video encoder on a bus network. Usually, for such a parallel system, the key concern is associated with partitioning and balancing of the computational load among the processors such that the overall processing time of the video encoder is minimized. With the use of the divisible load theory (DLT) paradigm, a strip-wise load partitioning/balancing scheme, a load distribution strategy, two implementation strategies are developed to exploit the data parallelism inherent in the video encoding process. The striking feature of our design is that,both the granularity of the load partitions and all the associated overheads caused during parallel video encoding process can be explicitly considered. This significantly contributes to the minimization of the overall processing time of the video encoder. Extensive experimental studies are carried out to test the effectiveness of the proposed strategies. The performance of the parallel video encoder is quantified using the metrics speedup and performance gain, respectively. The experimental results show that our strategies are effective for exploiting the available parallelism inherent in the video encoding process and provide a theoretical insight on how to analytically quantify and minimize the overall processing time of a parallel system. The proposed strategies can be easily extended and applied to improve other existing parallel systems.

Active Visual Segmentation

Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples

Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l0 norm, even though, in practice, the l1 or the lp ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly improved performance in its reconstruction from compressive samples. We also successfully incorporate the GI into a stochastic optimization algorithm for signal reconstruction from compressive samples and illustrate our approach with both synthetic and real signals/images.

A comparative study of efficient generalised Hough transform techniques

The generalised Hough transform (GHT) is useful for detecting or locating translated two-dimensional objects. However, a weakness of the GHT is its storage requirements and hence the increased computational complexity resulting from the four-dimensional parameter space. In this paper, we present the results of our work which involves investigation of the performance of several efficient GHT techniques including an extension of Thomass rotation-invariant algorithm. It is shown that our extension of Thomass algorithm has very low memory requirements and computational complexity, and produces the best results in various tests.