Tatung University

About: Tatung University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Fuzzy logic & Fuzzy control system. The organization has 3055 authors who have published 4578 publications receiving 80483 citations.

A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile

Abstract: To represent an image of high perceptual quality with the lowest possible bit rate, an effective image compression algorithm should not only remove the redundancy due to statistical correlation but also the perceptually insignificant components from image signals. In this paper, a perceptually tuned subband image coding scheme is presented, where a just-noticeable distortion (JND) or minimally noticeable distortion (MND) profile is employed to quantify the perceptual redundancy. The JND profile provides each signal being coded with a visibility threshold of distortion, below which reconstruction errors are rendered imperceptible. Based on a perceptual model that incorporates the threshold sensitivities due to background luminance and texture masking effect, the JND profile is estimated from analyzing local properties of image signals. According to the sensitivity of human visual perception to spatial frequencies, the full-band JND/MND profile is decomposed into component JND/MND profiles of different frequency subbands. With these component profiles, perceptually insignificant signals in each subband can be screened out, and significant signals can be properly encoded to meet the visibility threshold. A new quantitative fidelity measure, termed as peak signal-to-perceptible-noise ratio (PSPNR), is proposed to assess the quality of the compressed image by taking the perceptible part of the distortion into account. Simulation results show that near-transparent image coding can be achieved at less than 0.4 b/pixel. As compared to the ISO-JPEG standard, the proposed algorithm can remove more perceptual redundancy from the original image, and the visual quality of the reconstructed image is much more acceptable at low bit rates.

A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Abstract: Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates.