Fast Color Space Transformations Using Minimax Approximations

TL;DR

This paper introduces MACT, a novel method using minimax approximations to perform color space transformations efficiently, balancing speed, accuracy, and memory for real-time image processing applications.

Contribution

The paper presents MACT, a new approach that simplifies nonlinear color space transformations with minimax approximations, improving speed and resource efficiency.

Findings

MACT achieves high accuracy in color space transformations.

MACT outperforms traditional lookup table methods in speed and memory usage.

Extensive experiments validate MACT's effectiveness across diverse images.

Abstract

Color space transformations are frequently used in image processing, graphics, and visualization applications. In many cases, these transformations are complex nonlinear functions, which prohibits their use in time-critical applications. In this paper, we present a new approach called Minimax Approximations for Color-space Transformations (MACT).We demonstrate MACT on three commonly used color space transformations. Extensive experiments on a large and diverse image set and comparisons with well-known multidimensional lookup table interpolation methods show that MACT achieves an excellent balance among four criteria: ease of implementation, memory usage, accuracy, and computational speed.