Just Noticeable Difference (JND) Threshold for Each Image Pixel

This model computes the Just Noticeable Difference (JND) threshold for each pixel in an image, originally proposed by Chou. The algorithm analyzes luminance variations between adjacent pixels to determine perceptual sensitivity thresholds. Key implementation aspects include: - Spatial masking computation using local luminance contrast - Contrast sensitivity function modeling human visual perception - Edge detection and texture masking components The model holds significant importance in image processing and computer vision by quantifying human visual perception of subtle image variations. Practical applications include: - Image compression optimization through perceptual redundancy removal - Objective image quality assessment metrics - Visual attention modeling in computer vision systems The core implementation typically involves calculating base luminance thresholds combined with texture masking effects using Gaussian filters and contrast normalization. This framework provides fundamental tools for advanced image processing research and visual perception studies.