Pixelator v2: A Novel Perceptual Image Comparison Method with LAB Colour Space and Sobel Edge Detection for Enhanced Security Analysis

In this paper, we introduce Pixelator v2, a novel perceptual image comparison method designed to enhance security and analysis through improved image difference detection. Unlike traditional metrics such as MSE, Q, and SSIM, which often fail to capture subtle but critical changes in images, Pixelator v2 integrates the LAB (CIE-LAB) colour space for perceptual relevance and Sobel edge detection for structural integrity. By combining these techniques, Pixelator v2 offers a more robust and nuanced approach to identifying variations in images, even in cases of minor modifications. The LAB colour space ensures that the method aligns with human visual perception, making it particularly effective at detecting differences that are less visible in RGB space. Sobel edge detection, on the other hand, emphasises structural changes, allowing Pixelator v2 to focus on the most significant areas of an image. This combination makes Pixelator v2 ideal for applications in security, where image comparison plays a vital role in tasks like tamper detection, authentication, and analysis. We evaluate Pixelator v2 against other popular methods, demonstrating its superior performance in detecting both perceptual and structural differences. Our results indicate that Pixelator v2 not only provides more accurate image comparisons but also enhances security by making it more difficult for subtle alterations to go unnoticed. This paper contributes to the growing field of image-based security systems by offering a perceptually-driven, computationally efficient method for image comparison that can be readily applied in information system security.