Existing pixel‐based just noticeable distortion (JND) models only take into account luminance adaptation and texture masking (TM). Similarly, existing discrete cosine transform (DCT) based models do not take into account foveal vision effects and do not estimate TM efficiently. As human visual system (HVS) is not sensitive to distortion below the JND threshold, estimation of the perceptual visibility threshold is widely used in digital and video processing applications. The authors propose a comprehensive and efficient pixel‐based JND model incorporating all major factors which contribute to the JND estimation. The evaluation of contrast masking (CM) is done by distinguishing the edge and TM with respect to the entropy masking properties of the HVS. Similarly, the foveal vision effects are also taken into account for the comprehensive estimation of contrast sensitivity function (CSF). Hence, the proposed pixel‐based JND model incorporates the spatio‐temporal CSF, foveal vision effects, influence of eye‐movement, luminance adaptation and CM to be more consistent with human perception. The incorporation of these important factors makes the proposed model the most comprehensive and efficient in the current literature. Psychophysical experiments were performed to test the proposed model. The results show the proposed model comprehensively outperforms other existing models proving its efficiency.

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

This work investigates several approaches for implementing the OFDM functions of the fixed-WiMax standard on reconfigurable platforms. In the first phase, a custom RTL approach, using VHDL, is investigated. The approach shows the capability of a medium-size FPGA to accommodate the OFDM functions of a fixed-WiMax transceiver with only 50% occupation rate. In the second phase, a high-level approach based on the AccelDSP tool is used and compared to the custom RTL approach. The approach presents an easy flow to transfer MATLAB floating-point code into synthesizable cores. The AccelDSP approach shows an area overhead of 10%, while allowing early architectural exploration and accelerating the design time by a factor of two. However, the performance figure obtained is almost 1/4 of that obtained in the custom RTL approach. In the third phase, the Tensilica Xtensa configurable processor is targeted, which presents remarkable figures in terms of power, area, and design time. Comparing the three approaches indicates that the custom RTL approach has the lead in terms of performance. However, both the AccelDSP and the Tensilica Xtensa approaches show fast design time and early architectural exploration capability. In terms of power, the obtained estimation results show that the configurable Xtensa processor approach has the lead, where approximately the total power consumed is about 12--15 times less than those results obtained by the other two approaches.

The IEEE 802.16 standard, WiMAX, is a promising solution for broadband wireless access with implementations already happening. Those implementations are considering FPGAs as a viable option, because of their flexibility, performance, time-to-market and available resources. In this paper, two approaches are employed to map the IEEE 802.16-2004 standard on FPGAs: a pure VHDL and an AccelDSP-based approaches. Mapping the OFDM part of the digital baseband processor resulted an occupation rate of 25% and 30% resepctively. Timing results showed that the VHDL approach can obtain a better maximum frequency of operation of 171.8 MHz. However, the AccelDSP approach shows an advantage of a shorter design time and early trade-off analysis.

This paper describes a window based method for designing near perfect-reconstruction prototype filters for highly oversampled, complex modulated filter banks. The design method extends some well-known simple methods for critically sampled filter banks to the over-sampled case and to the case of different length analysis and synthesis filters. This design method is simple and effective for designing a large range of filter bank configurations. The design method is particularly useful in developing audio applications using oversampled filter banks where the target system's requirements are highly variable. The simplicity and flexibility of the design method means that this one method can be used to generate multiple prototype filters as the application requirements change.

The partitioned iterated function systems (PIFS) fractal image compression technique provides very competitive rate-distortion curves and fast decoding. However, it suffers from complicated encoding computation. Three novel neural network techniques, mixture of nonlinear principal components (MNLPC), mixture of independent components (MIC) and high-dimensional mixture of principal components (H-MPC) are developed to reduce the encoding complexity of the PIFS fractal coding. Applying these new techniques, the potential best range-domain matching search is confined to a relatively small size domain block pool. Using the new techniques, the encoding time is shortened dramatically, and the compression performance is improved as well.

A comprehensive color edge comparison across a variety color spaces is presented. Color adaptations of several well known edge detectors are compared in RGB, XYZ, CIELAB, CIELUV, rgb, l/sub 1/l/sub 2/l/sub 3/ and h/sub 1/h/sub 2/h/sub 3/-a new color space. The edge detectors studied are the Sobel operator, the modified Roberts operator, the vector gradient operator, and the 3/spl times/3 difference vector operator. Pratt's (1991) figure of merit is used for quantitative evaluation. The results indicate that the Sobel operator has the best performance across the edge detectors being compared and that results appear best for the h/sub 1/h/sub 2/h/sub 3/ color space.

This paper presents a tutor a overview of neural networks as signal processing tools for image compression. They are well suited to the problem of image compression due to their massively parallel and distributed architecture. Their characteristics are analogous to some of the features of our own visual system, which allow us to process visual information with much ease. For example, multilayer perceptions can be used as nonlinear predictors in differential pulse-code modulation (DPCM). Such predictors have been shown to increase the predictive gain relative to a linear predictor. Another active area of research is in the application of Hebbian learning to the extraction of principal components, which are the basis vectors for the optimal linear Karhunen-Loeve transform (KLT). These learning algorithms are iterative, have some computational advantages over standard eigendecomposition techniques, and can be made to adapt to changes in the input signal. Yet another model, the self-organizing feature map (SOFM), has been used with a great deal of success in the design of codebooks for vector quantization (VQ). The resulting codebooks are less sensitive to initial conditions than the standard LBG algorithm, and the topological ordering of the entries can be exploited to further increasing the coding efficiency and reducing the computational complexity.>

The optimal linear block transform for coding images is well known to be the Karhunen-Loeve transformation (KLT). However, the assumption of stationarity in the optimality condition is far from valid for images. Images are composed of regions whose local statistics may vary widely across an image. While the use of adaptation can result in improved performance, there has been little investigation into the optimality of the criterion upon which the adaptation is based. In this paper we propose a new transform coding method in which the adaptation is optimal. The system is modular, consisting of a number of modules corresponding to different classes of the input data. Each module consists of a linear transformation, whose bases are calculated during an initial training period. The appropriate class for a given input vector is determined by the subspace classifier. The performance of the resulting adaptive system is shown to be superior to that of the optimal nonadaptive linear transformation. This method can also be used as a segmentor. The segmentation it performs is independent of variations in illumination. In addition, the resulting class representations are analogous to the arrangement of the directionally sensitive columns in the visual cortex.

Proposes a novel approach to segmentation using a combination of Hebbian learning and competitive learning in a self-organizing manner. The network is modular, with each module corresponding to a different class of the input data. A module consists of a weight vector that is calculated during an initial training period. The appropriate class for a given input vector is determined by a maximum entropy classifier. The resulting network consistently extracts perceptually relevant features from image data. As well, the class representations are analogous to the arrangement of directionally sensitive columns in the visual cortex.>