TY - JOUR
T1 - Perceptually optimized image rendering
AU - Laparra, Valero
AU - Berardino, Alexander
AU - Ballé, Johannes
AU - Simoncelli, Eero P.
N1 - Funding Information:
Acknowledgment. JB and EPS are supported by the Howard Hughes Medical Institute. VL is supported by the Generalitat Valenciana grant (Spain) and Analog Devices Inc. AB is supported by the NEI Visual Neuroscience Training Program. We are grateful to a number of colleagues that have provided helpful comments during the development of this work, including Jesús Malo, Javier Calpe, Pau Seguí, Jorge Pérez, Marcelo Bertalmío, Ted Adelson, Alejandro Párraga, Xim Cerdá, Sylvian Paris, Mark Fairchild, and the members of the Laboratory for Computational Vision at NYU.
Publisher Copyright:
© 2017 Optical Society of America.
PY - 2017/9
Y1 - 2017/9
N2 - We develop a framework for rendering photographic images by directly optimizing their perceptual similarity to the original visual scene. Specifically, over the set of all images that can be rendered on a given display, we minimize the normalized Laplacian pyramid distance (NLPD), a measure of perceptual dissimilarity that is derived from a simple model of the early stages of the human visual system. When rendering images acquired with a higher dynamic range than that of the display, we find that the optimization boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the-art methods, but without manual intervention or parameter adjustment. We also demonstrate the effectiveness of the framework for a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). We show that the method may generally be used to enhance details and contrast, and, in particular, can be used on images degraded by optical scattering (e.g., fog). Finally, we demonstrate the necessity of each of the NLPD components—an initial power function, a multiscale transform, and local contrast gain control—in achieving these results and we show that NLPD is competitive with the current state-of-the-art image quality metrics.
AB - We develop a framework for rendering photographic images by directly optimizing their perceptual similarity to the original visual scene. Specifically, over the set of all images that can be rendered on a given display, we minimize the normalized Laplacian pyramid distance (NLPD), a measure of perceptual dissimilarity that is derived from a simple model of the early stages of the human visual system. When rendering images acquired with a higher dynamic range than that of the display, we find that the optimization boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the-art methods, but without manual intervention or parameter adjustment. We also demonstrate the effectiveness of the framework for a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). We show that the method may generally be used to enhance details and contrast, and, in particular, can be used on images degraded by optical scattering (e.g., fog). Finally, we demonstrate the necessity of each of the NLPD components—an initial power function, a multiscale transform, and local contrast gain control—in achieving these results and we show that NLPD is competitive with the current state-of-the-art image quality metrics.
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U2 - 10.1364/JOSAA.34.001511
DO - 10.1364/JOSAA.34.001511
M3 - Article
C2 - 29036154
AN - SCOPUS:85028562363
SN - 1084-7529
VL - 34
SP - 1511
EP - 1525
JO - Journal of the Optical Society of America A: Optics and Image Science, and Vision
JF - Journal of the Optical Society of America A: Optics and Image Science, and Vision
IS - 9
ER -