Content-Aware Automated Parameter Tuning for Approximate Color Transforms
This work addresses the practical need for content-aware parameter tuning in display technologies to save energy, representing an incremental improvement over existing approximate color transforms.
The paper tackles the problem of automatically tuning parameters for approximate color transforms to reduce display power consumption while preserving perceptual quality, achieving up to 50% power savings with negligible visual impairment as reported by users.
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.