Atsushi KOIKE

Multiview images captured by multicamera systems are generally not uniform in colour domain. In this paper, we propose a novel colour correction method of multicamera systems that can (i) be applied to not only dense multicamera system, but also sparse multicamera configuration and (ii) obtain an average colour pattern among all cameras. Our proposed colour correction method starts from any camera on the array sequentially, following a certain path, for pairs of cameras, until it reaches the starting point and triggers several iterations. The iteration stops when the correction applied to the images becomes small enough. We propose to calculate the colour correction transformation based on energy minimisation using a dynamic programming of a nonlinearly weighted Gaussian-based kernel density function of geometrically corresponding feature points, obtained by the modified scale invariant feature transformation (SIFT) method, from several time instances and their Gaussian-filtered images. This approach guarantees the convergence of the iteration procedure without any visible colour distortion. The colour correction is done for each colour channel independently. The process is entirely automatic, after estimation of the parameters through the algorithm. Experimental results show that the proposed iteration-based algorithm can colour-correct the dense/sparse multicamera system. The correction is always converged with average colour intensity among viewpoint, and out-performs the conventional method. (C) 2010 Elsevier Inc. All rights reserved.

The authors study a method for objective measurement of perceived picture quality for high definition video based on the full reference framework. The proposed method applies seven spatio-temporal image features to estimate perceived quality of pictures degraded by compression coding. Computer simulation shows that the proposed method can estimate perceived picture quality at a correlation coefficient of above 0.91. © 2009 SPIE-IS&amp T.

In this paper, the authors propose a novel method to measure the perceived picture quality of H.264 coded video; based on hybrid no reference framework. The latter term means that the proposed model uses only receiver-side information for objective video quality assessment, but analyzes both the compressed bitstream and baseband signals of the decoded picture to improve the estimation accuracy of the subjective quality. The proposed method extracts quantizer-scale information from the bit-stream along with two spatiotemporal image features from the baseband signal, which are integrated to express the overall quality using the weighted Minkowski metric. A computer simulation shows the proposed method can estimate the subjective quality at a correlation coefficient of 0.909 whereas the PSNR metric, which is referred to as a benchmark, correlates the subjective quality at a correlation coefficient of 0.773.

The authors study an objective method to estimate perceptual picture quality of the coded pictures based on No Reference framework. The proposed method extracts two image features that reflect the significance of blocking and flickering artifacts and estimates the subjective quality by weighted sum of the image features. Computer simulations show that the proposed method can estimate subjective quality at a correlation coefficient of 0.754.