Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

Technique for detecting a watermark in a marked image

Abstract: Apparatus and an accompanying method, for generating a cryptographic identifier for a non-marked image and embedding that identifier within the image itself in order to generate a “watermarked” image; for subsequently detecting that watermark in a test image; and the watermarked image so formed. First, pixel values for a non-marked image are transformed, either directly or after being enhanced, into a series of transform coefficients. A set of pseudo-random perturbation values which collectively constitute the watermark is determined wherein each of these values is heuristically selected, such that all these values collectively satisfy a plurality of different mathematical constraints and each such value preferably equals a relatively small value in a predefined range. These perturbation values are then added to the transform coefficients. Resulting perturbed coefficients are then inversely transformed back to pixel values to form the watermarked image. To detect whether a test image contains the watermark and hence is a copy of the watermarked image, the pixel values for the test image are transformed to yield transform coefficients. A plurality of different mathematical tests or a majority type rule is then used, in conjunction with the perturbation values, previously used to create the watermark, and these transform coefficients to determine whether the perturbation values collectively exist in the test image, and hence whether the watermark is present or not.

Image quality analysis with test pattern

Abstract: A system for the automated analysis of image quality obtained by a camera in a camera tunnel system includes a test pattern on an item for placement in the camera tunnel system and an imaging subsystem configured to capture an image of the item using the camera tunnel system, wherein the image includes an image of the test pattern. The system further includes an image analysis tool configured to automatically identify and analyze the image of the test pattern for generating one or more image quality metrics.

Image retrieval using blob histograms

Abstract: We present a new method for image indexing and retrieval that is based on pixel statistics from varying spatial scales. The proposed method employs a structuring element to determine the frequency distribution of pixels locally in the image and to detect local groups of pixels with uniform color or texture attributes. The frequency distribution and relative sizes of such groups are summarized into a table termed as a blob histogram. By embedding spatial information, color blob histograms are able to distinguish images that have the same color pixel distribution but contain objects with different sizes or shapes, without the need for segmentation. Using isotropic structuring elements, blob histograms are invariant to rotations and translations of the objects in an image. Experimental results of using blob histograms in image retrieval are given in the paper.

Psychophysical evaluation of gamut mapping techniques using simple rendered images and artificial gamut boundaries

Abstract: Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C/sup *//L/sup */) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.