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In the era of digital television, artifacts such as false contours unnoticed in small television sets appear conspicuously as the size of display devices increases. This paper proposes a two-stage false contour detection algorithm and its application to false contour reduction. In stage 1 of false contour detection, smooth regions are first removed by bit-depth reduction or re-quantization. In stage 2, false contours are separated from edges or texture regions using directional contrast features, yielding the binary false contour decision map that shows possible candidate regions of false contours. Also a number of non-directional and directional features for possible .false contour detection are considered and their performance is compared. For false contour reduction, variable-size directional smoothing is applied only to candidate regions that are specified by the false contour decision map. We use one-dimensional variable-size directional smoothing masks whose directions are orthogonal to the directions determined by the directional contrast features found in the .false contour detection step. Computer simulations with several test images with various types of false contours show the effectiveness of the proposed false contour reduction algorithm in terms of visual quality and the peak signal-to-noise ratio. The proposed algorithm can also be used as a post-processing method for artifact reduction in various display devices. Index Terms--Bit-depth, directional contrast feature, displays, false contour, false contour decision map, false contour reduction, re-quantization, two-stage, 1-dimensional directional smoothing filter, variable-size.

We propose rapid hybrid interpolation methods that employ more than one interpolation algorithm, and choose the most appropriate interpolation algorithm that provides high-quality images with a minimum number of operations. Although a complex interpolation algorithm generally outperforms a simple interpolation algorithm, the differences are negligible for most pixels, with major differences occurring around edges. Thus, in the proposed algorithm, we first apply a test to predict which interpolation is most appropriate for a given pixel in terms of complexity and performance. Then, a simple interpolation algorithm is used for pixels for which the simple interpolation algorithm provides acceptable performances, and a complex interpolation algorithm is used for pixels for which the complex interpolation algorithm significantly outperforms the simple interpolation algorithm. Consequently, it is possible to obtain high-quality images without significantly increasing the number of operations. © 2004 Society of Photo-Optical Instrumentation Engineers.

We propose a new method for an objective measurement of video quality. By analyzing subjective scores of various video sequences, we find that the human visual system is particularly sensitive to degradation around edges. In other words, when edge areas of a video sequence are degraded, evaluators tend to give low quality scores to the video, even though the overall mean squared error is not large. Based on this observation, we propose an objective video quality measurement method based on degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and locate edge areas. Then, we measure degradation of those edge areas by computing mean squared errors and use it as a video quality metric after some postprocessing. Experiments show that the proposed method significantly outperforms the conventional peak signal-to-noise ratio (PSNR). This method was also independently evaluated by independent laboratory groups in the Video Quality Experts Group (VQEG) Phase 2 test. The method consistently provided good performances. As a result, the method was included in international recommendations for objective video quality measurement.