Content-Aware Automated Parameter Tuning for Approximate Color Transforms

TL;DR

This paper introduces a content-aware, computationally-efficient method for determining lower bounds of parameters in approximate color transforms, enabling power savings with minimal perceptual quality loss.

Contribution

It presents a new heuristic-based model for predicting color transform parameters that balances power efficiency and perceptual quality, validated through extensive user studies.

Findings

Achieves up to 50% power saving in color transforms.

Predicts parameter lower bounds with 1.6% mean squared error.

Model correlates strongly with user perception (r>0.7).

Abstract

There are numerous approximate color transforms reported in the literature that aim to reduce display power consumption by imperceptibly changing the color content of displayed images. To be practical, these techniques need to be content-aware in picking transformation parameters to preserve perceptual quality. This work presents a computationally-efficient method for calculating a parameter lower bound for approximate color transform parameters based on the content to be transformed. We conduct a user study with 62 participants and 6,400 image pair comparisons to derive the proposed solution. We use the user study results to predict this lower bound reliably with a 1.6% mean squared error by using simple image-color-based heuristics. We show that these heuristics have Pearson and Spearman rank correlation coefficients greater than 0.7 (p<0.01) and that our model generalizes beyond the data from the user study. The user study results also show that the color transform is able to achieve up to 50% power saving with most users reporting negligible visual impairment.