Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: For situations where digital image transmission time and costs should be minimized, Wavelet image compression to 15 KB is recommended, although there is a slight cost of computational time.
Abstract: PURPOSE. To investigate image compression of digital retinal images and the effect of various levels of compression on the quality of the images. METHODS. JPEG (Joint Photographic Experts Group) and Wavelet image compression techniques were applied in five different levels to 11 eyes with subtle retinal abnormalities and to 4 normal eyes. Image quality was assessed by four different methods: calculation of the root mean square (RMS) error between the original and compressed image, determining the level of arteriole branching, identification of retinal abnormalities by experienced observers, and a subjective assessment of overall image quality. To verify the techniques used and findings, a second set of retinal images was assessed by calculation of RMS error and overall image quality. RESULTS. Plots and tabulations of the data as a function of the final image size showed that when the original image size of 1.5 MB was reduced to 29 KB using JPEG compression, there was no serious degradation in quality. The smallest Wavelet compressed images in this study (15 KB) were generally still of acceptable quality. CONCLUSIONS. For situations where digital image transmission time and costs should be minimized, Wavelet image compression to 15 KB is recommended, although there is a slight cost of computational time. Where computational time should be minimized, and to remain compatible with other imaging systems, the use of JPEG compression to 29 KB is an excellent alternative.
59 citations
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18 Jul 1997TL;DR: In this paper, an observer assists in generating an attentional map for each image of a test image sequence, which provides different thresholds or weighting factors for different areas of each image.
Abstract: Attentional maps that reflect the subjective view of an observer to the effects of degradation in a video image are used in the objective measurement of video quality degradation. The observer assists in generating an attentional map for each image of a test image sequence, which provides different thresholds or weighting factors for different areas of each image. A video image sequence from a system under test is compared with the test image sequence, and the error results are displayed as a function of the corresponding attentional maps.
58 citations
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24 Jun 2009TL;DR: In this article, a 3D video image processing method was proposed to acquire three-dimensional format information of a video image generated from video data to determine a 3-D format of the video image.
Abstract: A 3D video image processing method including: acquiring three-dimensional (3D) format information of a video image generated from video data to determine a 3D format of the video image; generating, from a first graphic image, a second graphic image corresponding to the determined 3D format of the video image using the 3D format information, the first graphic image being generated from graphic data; and overlaying the video image with the second graphic image.
58 citations
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TL;DR: The proposed method is a competitive and reliable methodology for blood vessels segmentation and shows a better performance than comparative methods, such as the threshold for a Frangi filter, Adaptive Threshold, and multiple classes Otsu method.
58 citations
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TL;DR: A set of intuitively motivated features are proposed for the detection of seam-carving using a pattern recognition approach and a Support Vector Machine based classifier is utilized to estimate which of the two classes the test image lies in.
Abstract: Content-aware resizing methods have recently been developed, among which, seam-carving has achieved the most
widespread use. Seam-carving's versatility enables deliberate object removal and benign image resizing, in which
perceptually important content is preserved. Both types of modifications compromise the utility and validity of the
modified images as evidence in legal and journalistic applications. It is therefore desirable that image forensic techniques
detect the presence of seam-carving. In this paper we address detection of seam-carving for forensic purposes. As in
other forensic applications, we pose the problem of seam-carving detection as the problem of classifying a test image in
either of two classes: a) seam-carved or b) non-seam-carved. We adopt a pattern recognition approach in which a set of
features is extracted from the test image and then a Support Vector Machine based classifier, trained over a set of
images, is utilized to estimate which of the two classes the test image lies in. Based on our study of the seam-carving
algorithm, we propose a set of intuitively motivated features for the detection of seam-carving. Our methodology for
detection of seam-carving is then evaluated over a test database of images. We demonstrate that the proposed method
provides the capability for detecting seam-carving with high accuracy. For images which have been reduced 30% by
benign seam-carving, our method provides a classification accuracy of 91%.
58 citations