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22
Issues in Vision Modeling for Perceptual Video Quality Assessment
, 1999
"... Lossy compression algorithms used in digital video systems produce artifacts whose visibility strongly depends on the actual image content. Simple error measures such as RMSE or PSNR, albeit popular, ignore this important fact and are only a mediocre predictor of perceived quality. Many applications ..."
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Cited by 78 (11 self)
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Lossy compression algorithms used in digital video systems produce artifacts whose visibility strongly depends on the actual image content. Simple error measures such as RMSE or PSNR, albeit popular, ignore this important fact and are only a mediocre predictor of perceived quality. Many applications require more reliable assessment methods. This paper discusses issues in vision modeling for perceptual video quality assessment (PVQA). Its purpose is not to describe a particular model or system, but rather to summarize and to provide pointers to up-to-date knowledge of important characteristics of the human visual system, to explain how these characteristics may be incorporated in vision models for PVQA, to give a brief overview of the state-of-the-art and current efforts in this field, and to outline directions for future research.
Video and image bayesian demosaicing with a two color image prior
- In ECCV
, 2006
"... Abstract. The demosaicing process converts single-CCD color representations of one color channel per pixel into full per-pixel RGB. We introduce a Bayesian technique for demosaicing Bayer color filter array patterns that is based on a statistically-obtained two color per-pixel image prior. By modeli ..."
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Cited by 13 (4 self)
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Abstract. The demosaicing process converts single-CCD color representations of one color channel per pixel into full per-pixel RGB. We introduce a Bayesian technique for demosaicing Bayer color filter array patterns that is based on a statistically-obtained two color per-pixel image prior. By modeling all local color behavior as a linear combination of two fully specified RGB triples, we avoid color fringing artifacts while preserving sharp edges. Our grid-less, floating-point pixel location architecture can process both single images and multiple images from video within the same framework, with multiple images providing denser color samples and therefore better color reproduction with reduced aliasing. An initial clustering is performed to determine the underlying local two color model surrounding each pixel. Using a product of Gaussians statistical model, the underlying linear blending ratio of the two representative colors at each pixel is estimated, while simultaneously providing noise reduction. Finally, we show that by sampling the image model at a finer resolution than the source images during reconstruction, our continuous demosaicing technique can super-resolve in a single step. 1
Perceptual quality assessment of color images using adaptive signal representation
, 2010
"... Perceptual image distortion measures can play a fundamental role in evaluating and optimizing imaging systems and image processing algorithms. Many existing measures are formulated to represent “just noticeable differences” (JNDs), as measured in psychophysical experiments on human subjects. But som ..."
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Cited by 4 (1 self)
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Perceptual image distortion measures can play a fundamental role in evaluating and optimizing imaging systems and image processing algorithms. Many existing measures are formulated to represent “just noticeable differences” (JNDs), as measured in psychophysical experiments on human subjects. But some image distortions, such as those arising from small changes in the intensity of the ambient illumination, are far more tolerable to human observers than those that disrupt the spatial structure of intensities and colors. Here, we introduce a framework in which we quantify these perceptual distortions in terms of “just intolerable differences ” (JIDs). We first construct a set of spatio-chromatic basis functions to approximate (as a first-order Taylor series) a set of “non-structural ” distortions that result from changes in lighting/imaging/viewing conditions. These basis functions are defined on local image patches, and are adaptive, in that they are computed as functions of the undistorted reference image. This set is then augmented with a complete basis arising from a linear approximation of the CIELAB color space. Each basis function is weighted by a scale factor to convert it into units corresponding to JIDs. Each patch of the error image is represented using this weighted overcomplete basis, and the overall distortion metric is computed by summing the squared coefficients over all such (overlapping) patches. We implement an example of this metric, incorporating invariance to small changes in the viewing and lighting conditions, and demonstrate that the resulting distortion values are more consistent with human perception than those produced by CIELAB or S-CIELAB.
Performance of the euclidean color-difference formula in log-compressed osa-ucs space applied to modified-image-difference metrics
- In 11th Congress of the International Colour Association (AIC
, 2009
"... In this paper, we approach color-image-difference metrics by a Euclidean color-difference formula for small-medium color differences in log-compressed OSA-UCS space, recently published (C. Oleari, M. Melgosa and R. Huertas, J. Opt. Soc. Am. A, 26(1):121–134, 2009). We start from previous image-diffe ..."
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Cited by 3 (2 self)
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In this paper, we approach color-image-difference metrics by a Euclidean color-difference formula for small-medium color differences in log-compressed OSA-UCS space, recently published (C. Oleari, M. Melgosa and R. Huertas, J. Opt. Soc. Am. A, 26(1):121–134, 2009). We start from previous image-difference metrics by replacing the CIE color-difference formulae with the new one. Tests are made by using the Pearson-, Spearman- and Kendall-correlation coefficient. Particularly, we compare the calculated image-difference metrics in relation to the perceived image difference obtained with psychophysical experiments. Current results show improvements in the actual state of art, making this formula the future key for image- difference metrics.
Fast Switching Filter for Impulsive Noise Removal from Color Images
"... Abstract. In this article, we present a fast switching filter for impulsive noise removal from color images. The filter exploits the hue, saturation, and lightness color space and is based on the peer group concept, which allows for the fast detection of noise in a neighborhood without resorting to ..."
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Cited by 3 (3 self)
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Abstract. In this article, we present a fast switching filter for impulsive noise removal from color images. The filter exploits the hue, saturation, and lightness color space and is based on the peer group concept, which allows for the fast detection of noise in a neighborhood without resorting to pairwise distance computations between each pixel. Experiments on large set of diverse images demonstrate that the proposed approach is not only extremely fast, but also gives excellent results in comparison to various state-ofthe-art
QUANTIFYING COLOR IMAGE DISTORTIONS BASED ON ADAPTIVE SPATIO-CHROMATIC SIGNAL DECOMPOSITIONS
, 2009
"... We describe a framework for quantifying color image distortion based on an adaptive signal decomposition. Specifically, local blocks of the image error are decomposed using a set of spatiochromatic basis functions that are adapted to the spatial and color structure of the original image. The adaptiv ..."
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Cited by 3 (2 self)
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We describe a framework for quantifying color image distortion based on an adaptive signal decomposition. Specifically, local blocks of the image error are decomposed using a set of spatiochromatic basis functions that are adapted to the spatial and color structure of the original image. The adaptive functions are chosen to isolate specific distortions such as luminance, hue, and saturation changes. These adaptive basis functions are used to augment a generic orthonormal basis, and the overall distortion is computed from the weighted sum of the coefficients of the resulting overcomplete decomposition, with smaller weights chosen for the adaptive terms. A set of preliminary experiments show that the proposed distortion measure is consistent with human perception of color images subjected to a variety of different common distortions. The framework may be easily extended to include any form of continuous spatio-chromatic distortion.
Evaluation of contrast measures in relation to observers perceived contrast,” in [CGIV 2008 - Fourth European Conference on Color in Graphics, Imaging and Vision ], 253–256
, 2008
"... We have carried out a psychophysical experiment to reg-ister perceived contrast. 17 observers viewed 15 images, each image was shown for 40 seconds where the observer stated the perceived contrast of the image. The results from the observers indicate that the consensus of contrast among experts decr ..."
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Cited by 2 (1 self)
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We have carried out a psychophysical experiment to reg-ister perceived contrast. 17 observers viewed 15 images, each image was shown for 40 seconds where the observer stated the perceived contrast of the image. The results from the observers indicate that the consensus of contrast among experts decreases as the perceived contrast decreases. Experts also rate the con-trast higher then non-experts. A number of contrast algorithms, developed to predict perceived contrast was evaluated against the perceived contrast from the observers.
By Entitled
, 2013
"... To the best of my knowledge and as understood by the student in the Research Integrity and Copyright Disclaimer (Graduate School Form 20), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy on Integrity in Research ” and the use of copyrighted material. Approved by Maj ..."
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To the best of my knowledge and as understood by the student in the Research Integrity and Copyright Disclaimer (Graduate School Form 20), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy on Integrity in Research ” and the use of copyrighted material. Approved by Major Professor(s): ____________________________________
