This paper presents a subjective experiment performed to determine the optimal level of sharpness enhancement for
various image content and two display technologies. For the experiment, a peaking algorithm was used as sharpness
enhancement method and its gain parameter, which controls the amount of sharpness enhancement, was optimized.
Eleven still images were used as image material, and for each original, eight levels of sharpness enhancement were
shown. Subjects were asked to select the image that they prefer most on overall image quality. To study the effect of the
display technology, the experiment was performed on a CRT monitor and LCD panel. The results show an effect of
content: lower gains were preferred for faces and content with flat areas, while higher gains were preferred for highly
textured images. We also found an effect of display: in general for a given image, the averaged gain preferred on the
CRT was equal or higher than on the LCD. The results can be used to optimize sharpness enhancement algorithms to
image content and display type in agreement with the averaged preference of viewers.
In this paper we present a no-reference objective quality metric (NROQM) that has resulted from extensive research on impairment metrics, image feature metrics, and subjective image quality in several projects in Philips Research, and participation in the ITU Video Quality Experts Group. The NROQM is aimed at requirements including video algorithm development, embedded monitoring and control of image quality, and evaluation of different types of display systems. NROQM is built from metrics for desirable and non-desirable image features (sharpness, contrast, noise, clipping, ringing, and blocking artifacts), and accounts for their individual and combined contributions to perceived image quality. We describe our heuristic, incremental approach to modeling quality and training the NROQM, and its advantages to deal with imperfect data and imperfect metrics. The results of training the NROQM using a large set of video sequences, which include degraded and enhanced video, show high correlation between objective and subjective scores, and the results of the first performance test show good objective-subjective correlations as well. We also discuss issues that require further research such as fully content-independent metrics, measuring over-enhanced video quality, and the role of temporal impairment metrics.