Most proposed video quality assessment approaches in the literature are only applied to the luminance layer of frame. Others are applied to luminance and chrominance layers respectively and then pooling by means of grayscale image quality metrics. In this paper, we proposed a full-reference objective video quality assessment called CMSVD,which treats luminance and chrominance parts as a whole and considers multiple factors of the human visual system (HVS)and video features. The algorithm is tested on the video quality experts group (VQEG) phase I FR-TV test data set.Experiments show that it has good correlation with perceived video quality.

This paper presents an approach for image recovery from broken image streams based on an image continuity model. The information to be recovered includes the header of an image (width, height, color space etc.) and the data stream which might have been partially damaged. We begin our discussion on common image formats and necessary information for the image recovery. We then define several measures on image contents to facilitate the image recovery. Using these measures, an effective recovery algorithm is proposed. Experimental results show that the algorithm can successfully recover images of different formats, even multi-frame images from broken image streams in most cases.

In this paper, we propose a fast and reliable impulse noise filter for highly corrupted images. The median filter was once the most popular nonlinear filter for removing impulse noise because of its good denoising power and computational efficiency, but the performance are unsatisfactory when noise ratio is high. So many algorithms have been proposed to improve the filter result. Recently, A switching median filter with boundary discriminative noise detection(BDND) was proposed, it is very effective and outperforms all previously proposed median-based filters, but it's very time-consuming in calculation. The proposed filter use similar scheme as in BDND, it is also very effective and much more efficient than BDND. The proposed method use a new powerful and efficient noise detection method to determine if current pixel has been corrupted by noise, if it's corrupted, a variable window median filter is used to attenuate impulse noise, while those uncorrupted pixels are remain unchanged. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.

In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.

Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.

Flower image retrieval is a very important step for computer-aided plant species identification. We propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of a flower and two shape-based sets of features, centroid-contour-distance (CCD) and angle code histogram (ACH), to characterize the shape features of a flower contour. Experimental results show that our flower region extraction approach based on color clustering and domain knowledge can achieve accurate flower regions. The retrieval results on a database of 885 flower images collected from 14 plant species show that our region-of-interest (ROI) based retrieval approach can perform better than the Swain method based on the global color histogram (Swain, M.J. and Ballard, D.H., Int. J. of Computer Vision, vol.7, no.1, p.11-32, 1991).

Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L(2)-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with the human visual perception in many cases. A fuzzy image metric (FIM) is defined based on Sugeno's (1977) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgment procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using the PSNR. Compared to the L(2)-metric, we demonstrate that a better performance can be achieved in fractal coding by using the proposed FIM.

Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L/sup 2/-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with human visual perception in many cases. In this paper, a fuzzy image metric (FIM) is defined based on M. Sugeno's (1974) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgement procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using PSNR.