Perceptually Optimized Image Rendering

TL;DR

This paper presents a perceptually optimized image rendering framework that enhances image quality by minimizing a human visual system-based dissimilarity measure, accommodating display limitations and improving degraded images.

Contribution

It introduces a novel constrained optimization approach using NLPD for perceptual image rendering, addressing display constraints and image degradation.

Findings

Boosts contrast of low-contrast features without artifacts

Achieves visual quality comparable to state-of-the-art methods

Enhances details in images degraded by optical scattering

Abstract

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).

Full text

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