The information about the blur and noise of an original image is lost when a standard image thumbnail is generated by filtering and subsampling. Image browsing becomes difficult since the standard thumbnails do not distinguish between high-quality and low-quality originals. In this paper, an efficient algorithm with a blur-generating component and a noise-generating component preserves the local blur and the noise of the originals. The local blur is rapidly estimated using a scale-space expansion of the standard thumbnail and subsequently used to apply a space-varying blur to the thumbnail. The noise is estimated and rendered by using multirate signal transformations that allow most of the processing to occur at the lower spatial sampling rate of the thumbnail. The new thumbnails provide a quick, natural way for users to identify images of good quality. A subjective evaluation shows the new thumbnails are more representative of their originals for blurry images. The noise generating component improves the results for noisy images, but degrades the results for textured images. The blur generating component of the new thumbnails may always be used to advantage. The decision to use the noise generating component of the new thumbnails should be based on testing with the particular image mix expected for the application.

Texture appearance is an important component of photographic image quality as well as object recognition. Noise cleaning algorithms are used to decrease sensor noise of digital images, but can hinder texture elements in the process. The Camera Phone Image Quality (CPIQ) initiative of the International Imaging Industry Association (I3A) is developing metrics to quantify texture appearance. Objective and subjective experimental results of the texture metric development are presented in this paper. Eight levels of noise cleaning were applied to ten photographic scenes that included texture elements such as faces, landscapes, architecture, and foliage. Four companies (Aptina Imaging, LLC, Hewlett-Packard, Eastman Kodak Company, and Vista Point Technologies) have performed psychophysical evaluations of overall image quality using one of two methods of evaluation. Both methods presented paired comparisons of images on thin film transistor liquid crystal displays (TFT-LCD), but the display pixel pitch and viewing distance differed. CPIQ has also been developing objective texture metrics and targets that were used to analyze the same eight levels of noise cleaning. The correlation of the subjective and objective test results indicates that texture perception can be modeled with an objective metric. The two methods of psychophysical evaluation exhibited high correlation despite the differences in methodology.

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