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Statistics of Cone Responses to Natural Images: Implications for Visual Coding
- Journal of the Optical Society of America A
, 1998
"... ted in the first stage of retinal processing, the photoreceptor layer. In this work we measure the spectral distributions of light present in natural images by using a hyperspectral camera, 12--15 which provides a complete spectrum at each pixel. We derive human cone responses at each spatial loc ..."
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Cited by 155 (2 self)
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ted in the first stage of retinal processing, the photoreceptor layer. In this work we measure the spectral distributions of light present in natural images by using a hyperspectral camera, 12--15 which provides a complete spectrum at each pixel. We derive human cone responses at each spatial location from the spectra, and from these we gather cone response statistics for analysis. This approach is related to that of Webster and Mollon, with the key difference that whereas they contrast the differences between various images, we study the ensemble statistics as averaged over images. Our results are qualitatively similar to those of Buchsbaum and Gottschalk, who sought to understand theoretically, by using model stimuli, how the visual system might decorrelate natural cone signals through an orthogonal linear transformation. They found that under certain conditions this can be achieved through a transformation to a luminancelike channel and a pair of blue-- yellow and red--gre
Color and luminance information in natural scenes
- Journal of the Optical Society of America A
, 1998
"... The spatial filtering applied by the human visual system appears to be low-pass for chromatic stimuli and band-pass for luminance stimuli. Here we explore whether this observed difference in contrast sensitivity reflects a real difference in the components of chrominance and luminance in natural sce ..."
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Cited by 50 (4 self)
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The spatial filtering applied by the human visual system appears to be low-pass for chromatic stimuli and band-pass for luminance stimuli. Here we explore whether this observed difference in contrast sensitivity reflects a real difference in the components of chrominance and luminance in natural scenes. For this purpose a digital set of 29 hyper-spectral images of natural scenes has been acquired and its spatial frequency content analyzed in terms of chrominance and luminance defined according to existing models of the human cone responses and visual signal processing. The statistical 1/f amplitude spatial frequency distribution is confirmed for a variety of chromatic conditions across the visible spectrum. Our analysis suggests that natural scenes are relatively rich in high spatial-frequency chrominance information which does not appear to be transmitted by the human visual system. This result is unlikely to have arisen from errors in the original measurements. Several reasons may combine to explain a failure to transmit high spatial-frequency chrominance: (a) its minor importance for primate visual tasks, (b) its removal by filtering applied to compensate for chromatic aberration of the eye's optics, or (c) a biological bottleneck blocking its transmission. In addition, we graphically compare the ratios of luminance to chrominance measured by our hyperspectral camera and those measured psychophysically over an equivalent spatial frequency range. 1
Image Capture: Simulation of Sensor Responses from Hyperspectral Images
- IEEE Trans. Image Processing
, 2001
"... This paper describes the design and performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to irradiance over m ..."
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Cited by 25 (2 self)
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This paper describes the design and performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to irradiance over most of its operating range. The input to the simulator is a set of narrow-band images of the scene taken with a custom-designed hyperspectral camera system. The parameters for the simulator are the number of sensor classes, the sensor spectral sensitivities, the noise statistics and number of quantization levels for each sensor class, the spatial arrangement of the sensors and the exposure duration. The output of the simulator is the raw image data that would have been acquired by the simulated image capture device.
Seasonal variations in the color statistics of natural images
- Network
, 2007
"... We examined how the distribution of colors in natural images varies as the seasons change. Images of natural outdoor scenes were acquired at locations in the Western Ghats, India, during monsoon and winter seasons and in the Sierra Nevada, USA, from spring to fall. The images were recorded with an R ..."
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Cited by 18 (9 self)
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We examined how the distribution of colors in natural images varies as the seasons change. Images of natural outdoor scenes were acquired at locations in the Western Ghats, India, during monsoon and winter seasons and in the Sierra Nevada, USA, from spring to fall. The images were recorded with an RGB digital camera calibrated to yield estimates of the L, M, and S cone excitations and chromatic and luminance contrasts at each pixel. These were compared across time and location and were analyzed separately for regions of earth and sky. Seasonal climate changes alter both the average color in scenes and how the colors are distributed around the average. Arid periods are marked by a mean shift toward the þL pole of the L vs.M chromatic axis and a rotation in the color distributions away from the S vs. LM chromatic axis and toward an axis of bluish–yellowish variation, both primarily due to changes in vegetation. The form of the change was similar at the two locations suggesting that the color statistics of natural images undergo a characteristic pattern of temporal variation. We consider the implications of these changes for models of both visual sensitivity and color appearance.
Simulation Of Digital Camera Images From Hyperspectral Input
"... INTRODUCTION An important goal for digital color cameras is to record enough information about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original. We can divide factors ..."
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Cited by 13 (4 self)
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INTRODUCTION An important goal for digital color cameras is to record enough information about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original. We can divide factors that aect the overall performance of a digital camera into two classes. The rst class is camera design parameters. These inuence the information recorded by the camera. Examples of design parameters include the spatial resolution of the camera, the spectral sensitivities of its sensors, and the reliability of the sensor responses. The second class of factors is the algorithms applied to the camera data. Camera design evaluation must take both classes of factors into account, particularly if they interact. The goal of this chapter is to describe a digital camera simulator that predicts the relation between a scene and the camera image. The input to the simulator is a high resolution hyper
The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction in Natural Images
"... Bandpass filtering, orientation selectivity, and contrast gain control are prominent features of sensory coding at the level of V1 simple cells. While the effect of bandpass filtering and orientation selectivity can be assessed within a linear model, contrast gain control is an inherently nonlinear ..."
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Cited by 9 (4 self)
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Bandpass filtering, orientation selectivity, and contrast gain control are prominent features of sensory coding at the level of V1 simple cells. While the effect of bandpass filtering and orientation selectivity can be assessed within a linear model, contrast gain control is an inherently nonlinear computation. Here we employ the class of Lp elliptically contoured distributions to investigate the extent to which the two features—orientation selectivity and contrast gain control—are suited to model the statistics of natural images. Within this framework we find that contrast gain control can play a significant role for the removal of redundancies in natural images. Orientation selectivity, in contrast, has only a very limited potential for redundancy reduction. 1
2 Bayesian Approaches to Bilinear Inverse Problems Involving Spatial Evidence: Color Constancy and Blind Image
, 2006
"... isor ..."
Color Constancy algorithms: psychophysical evaluation on a 1 new dataset 2 3 Color Constancy algorithms: psychophysical evaluation on a 25 new dataset 26 27
"... Abstract 8 The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer 9 vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution 10 from within the feasible set. ..."
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Abstract 8 The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer 9 vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution 10 from within the feasible set.
Long`ere
"... An important goal for digital color cameras is to record enough information about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original. ..."
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An important goal for digital color cameras is to record enough information about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original.
Chapter SIMULATION OFDIGITAL CAMERA IMAGES FROM HYPERSPECTRAL INPUT
"... An important goal for digital color cameras is to record enough in-formation about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original. ..."
Abstract
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An important goal for digital color cameras is to record enough in-formation about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original.
