# Perceptually Optimized Image Rendering

arXiv: https://arxiv.org/abs/1701.06641 · HTML: https://mlsanity.com/paper/1701.06641/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 15 of 15)

- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis](https://mlsanity.com/paper/2502.13196/) (2025)
- [Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping](https://mlsanity.com/paper/2109.00180/) (2021)
- [Perceptual Assessment and Optimization of HDR Image Rendering](https://mlsanity.com/paper/2310.12877/) (2023)
- [Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception](https://mlsanity.com/paper/2102.10951/) (2021)
- [A Perceptually Optimized and Self-Calibrated Tone Mapping Operator](https://mlsanity.com/paper/2206.09146/) (2022)
- [Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression](https://mlsanity.com/paper/2504.06301/) (2025)
- [Learned HDR Image Compression for Perceptually Optimal Storage and Display](https://mlsanity.com/paper/2407.13179/) (2024)
- [Assessing invariance to affine transformations in image quality metrics](https://mlsanity.com/paper/2407.17927/) (2024)
- [What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics](https://mlsanity.com/paper/2305.11582/) (2023)
- [Efficient joint noise removal and multi exposure fusion](https://mlsanity.com/paper/2112.03701/) (2021)
- [Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset](https://mlsanity.com/paper/2203.13558/) (2022)
- [Parametric PerceptNet: A bio-inspired deep-net trained for Image Quality Assessment](https://mlsanity.com/paper/2412.03210/) (2024)
- [Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation](https://mlsanity.com/paper/2607.03494/) (2026)
- [Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection](https://mlsanity.com/paper/2608.30400/) (2026)

## Abstract (arXiv)

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).