Computing Watson's DCT Visual Model

By implementing Watson's DCT (Discrete Cosine Transform) visual model, we can compute the JND (Just Noticeable Difference) values for coefficients in each 8x8 block. The JND value represents the minimum detectable difference in luminance or color perception under specified viewing conditions. This technique finds applications in image and video quality assessment frameworks, as well as other applications requiring visual perception analysis. From a code implementation perspective, this typically involves dividing the image into 8x8 blocks, applying DCT transformation, and then calculating JND thresholds based on Watson's frequency sensitivity models and masking effects. The algorithm can be optimized using matrix operations and lookup tables for efficient computation. Additionally, this methodology can enhance image and video compression algorithms by incorporating perceptual thresholds, thereby improving the quality of compressed visual content while maintaining optimal compression ratios.