Perceptually optimized image rendering

Valero Laparra, Alexander Berardino, Johannes Ballé, Eero P. Simoncelli

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1511-1525
Number of pages15
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number9
StatePublished - Sep 2017

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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