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.

Optimal histogram-pair and prediction-error based image reversible data hiding

Abstract: This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with the following four thresholds for optimal performance: embedding threshold, fluctuation threshold, left- and right-histogram shrinking thresholds. The embedding threshold is used to select only those prediction-errors, whose magnitude does not exceed this threshold, for possible reversible data hiding. The fluctuation threshold is used to select only those prediction-errors, whose associated neighbor fluctuation does not exceed this threshold, for possible reversible data hiding. The left- and right-histogram shrinking thresholds are used to possibly shrink histogram from the left and right, respectively, by a certain amount for reversible data hiding. Only when all of four thresholds are satisfied the reversible data hiding is carried out. Different from our previous work, the image gray level histogram shrinking towards the center is not only for avoiding underflow and/or overflow but also for optimum performance. The required bookkeeping data are embedded together with pure payload for original image recovery. The experimental results on four popularly utilized test images (Lena, Barbara, Baboon, Airplane) and one of the JPEG2000 test image (Woman, whose histogram does not have zero points in the whole range of gray levels, and has peaks at its both ends) have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image verses original image at given pure payloads.

Generalised block-matching motion estimation using quad-tree structured spatial decomposition

Abstract: A generalised block-matching motion estimation with variable-size blocks is proposed. The location and size of each block are determined from a quad-tree spatial decomposition algorithm. Experimental results with head-and-shoulders test image sequences show that motion-compensated errors with this method have lower entropy than the conventional fixed or variable-size block-matching techniques. The quality of the coded pictures under the proposed method with an H.261 codec, both subjectively and in terms of PSNR, outperforms the conventional use of fixed and variable block-size motion estimators.< >

Digital color copying machine comprising a test mode for making a color adjustment

Abstract: A digital color copying machine comprising a test mode is disclosed. In the test mode, image data corresponding to a partial area of an original document indicated is stored in a RAM, and thereafter, the image data stored in the RAM is read out repeatedly, and plural test images for which the color correction is made with different color balances respectively are formed as mosaic monitor images on a recording medium. Then, one of plural test images is selected, and a copy of document having a color balance of the selected test image is produced. The state of the selected color balance is displayed on a display section.

Automatic selection of color constancy algorithms for dark image enhancement by fuzzy rule-based reasoning

Abstract: Graphical abstractDisplay Omitted HighlightsA fuzzy rule-based system operates as a selector of color constancy algorithms.The system selects among the White-Patch, Gray-World and Gray-Edge algorithms.The method attains a high rate of correct selection according to the actual scene.Two problems are addressed simultaneously: color constancy and color enhancement.The framework can be used in engineering applications, like video surveillance. This work introduces a fuzzy rule-based system operating as a selector of color constancy algorithms for the enhancement of dark images. In accordance with the actual content of an image, the system selects among three color constancy algorithms, the White-Patch, the Gray-World and the Gray-Edge. These algorithms have been considered because of their accurate remotion of the illuminant, besides showing an outstanding color enhancement on images. The design of the rule-based system is not a trivial task because several features are involved in the selection. Our proposal consists in a fuzzy system, modeling the decision process through simple rules. This approach can handle large amounts of information and is tolerant to ambiguity, while addressing the problem of dark image enhancement. The methodology consists in two main stages. Firstly, a training protocol determines the fuzzy rules, according to features computed from a subset of training images taken from the SFU Laboratory dataset. We choose carefully twelve image features for the formulation of the rules: seven color features, three texture descriptors, and two lighting-content descriptors. In the rules, the fuzzy sets are modeled using Gaussian membership functions. Secondly, experiments are carried out using Mamdani and Larsen fuzzy inferences. For a test image, a color constancy algorithm is selected according to the inference process and the rules previously defined. The results show that our method attains a high rate of correct selection of the most well-suited algorithm for the particular scene.

System and method for medical image reconstruction and image series denoising using local low rank promotion

Abstract: A method for reconstructing a time series of images from data acquired with a medical imaging system is provided. Data is acquired with the medical imaging system, and a set of image blocks that defines the location and size of each of a plurality of image blocks in the image domain is then selected. The acquired data and selected image block set are then used to jointly estimate a plurality of images that form a time series of images while promoting locally-low rank structure in the images.