Research On Visual Perception Based Spatial Gamut Mapping

The problem of high fidelity color information reproduction has been a hot topic in the field of image processing with the development of displaying and outputting technologies.The gamut mapping algorithm is the key technology to solve the problem.Traditional gamut mapping algorithms often involve a loss of information,due to rarely taking into account the spatial information and characteristics of human visual perception.Firstly,this thesis proposes a gamut mapping optimization algorithm based on gamut mapped image measure(GMIM).This algorithm sets GMIM as an objective function,changing the length of step according to information weight.A new metric,GMIM,is developed to compute achromatic and chromatic differences of images.For the achromatic part,we introduce the gradient information to detect the achromatic distortions between two images;the chromatic part aims to analyze the hue and chroma information.Further,the circular statistical theory is employed to calculate the hue value.Experimental results demonstrated that our new gamut mapping method can preserve the brightness,color,and detail information of original images.Secondly,we design a gamut mapping algorithm based on visual perception and saliency.Our algorithm introduces saliency information into a variational framework based on visual perception.In order to retain details in gamut mapped images as much as possible,we formulate gamut mapping as the optimization of an energy function.Moreover,to reduce the saliency difference between the gamut mapped image and the original image,we introduce the saliency weight into the image energy function.We specially design a sampling strategy based on the human lateral inhibition mechanism to simplify the energy function and maintain the details,in which the pixels are selected around the target pixels in accordance with the characteristics of the human lateral inhibition.Our algorithm is better than tradition algorithms with both subjective evaluations and objective evaluations in retaining more details.