Adaptive Approximate Computing of Image Data with Low Gradient Feature

An adaptive spatial approximation algorithm is proposed while minimizing false contouring based on local image characteristics considering human visual systems to save the amount of image frame memory and power for image data transmission. The proposed algorithm is generalized by using block-based k-means clustering to categorize image blocks with the same characteristics, and the amount of approximation is evaluated for each cluster. Two different image quality standards are maintained to maximize image approximation while maintaining the required image qualities. The proposed algorithm can reduce frame buffer memory up to 35.11% on average.