We develop a framework for rendering photographic images, taking into account display limitations, so as to optimize perceptual similarity between the rendered image and the original scene. We formulate this as a constrained optimization problem, in which we minimize a measure of perceptual dissimilarity, the Normalized Laplacian Pyramid Distance (NLPD), which mimics the early stage transformations of the human visual system. When rendering images acquired with higher dynamic range than that of the display, we find that the optimized solution boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the art methods with no manual intervention or parameter settings. We also examine 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). Finally, we show that the method may be used to enhance details and contrast of images degraded by optical scattering (e.g., fog).
Two parts of (1) REF developed a framework that relies on four ingredients to optimize the perceptual similarity between the rendered image and the original scene, with the high-dimensional space of the rendered image also being computationally expensive.
Perceptually Optimized Image Rendering
Journal of the Optical Society of America. A, Optics, image science, and vision
Journal of the Optical Society of America. A, Optics, image science, and vision
Physics Medicine Computer Science