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.
Image thumbnails are used in most imaging products and applications, where they allow quick preview of the content of
the underlying high resolution images. The question: "How would you best represent a high resolution original image
given a fixed number of thumbnail pixels?" is addressed using both automatically and manually generated thumbnails.
Automatically generated thumbnails that preserve the image quality of the high resolution originals are first reviewed and
subjectively evaluated. These thumbnails allow interactive identification of image quality, while simultaneously allowing
the viewer's knowledge to select desired subject matter. Images containing textures are, however, difficult for the automatic
algorithm. Textured images are further studied by using photo editing to manually generate representative thumbnails.
The automatic thumbnails are subjectively compared to standard (filter and subsample) thumbnails using clean, blurry,
noisy, and textured images. Results using twenty subjects find the automatic thumbnails more representative of their
originals for blurry images. In addition, as desired, there is little difference between the automatic and standard thumbnails
for clean images. The noise component improves the results for noisy images, but degrades the results for textured images.
Further studying textured images, the manual thumbnails were subjectively compared to standard thumbnails for four
images. Evaluation using forty judgments found a bimodal distribution for preference between the standard and the manual
thumbnails, with some observers preferring manual thumbnails and others preferring standard thumbnails.