Quality evaluation for dual-band color fusion images based on scene understanding

Image quality assessments are the basis for evaluations of dual-band color fusion algorithms and systems. A method of quality evaluation for visible and infrared color fusion images was explored. A comprehensive evaluation metric, image perceptual quality based on scene understanding (PQSU) was proposed, and color fusion images of three typical scenes were selected to perform a psychophysical experiment. The prediction model of PQSU was derived by multiple linear regression analysis of the experimental data for conventional image quality metrics and the proposed evaluation metric. The results show that the positive correlation between color harmony and color naturalness is very high. The variation of PQSU can be predicted effectively by color harmony and sharpness. In the three image categories, the proportional coefficients in prediction models for PQSU are different; whereas, the basic forms of Image quality assessments are the basis for evaluations of dual-band color fusion algorithms and systems. A method of quality evaluation for visible and infrared color fusion images was explored. A comprehensive evaluation metric, image perceptual quality based on scene understanding (PQSU) was proposed, and color fusion images of three typical scenes were selected to perform a psychophysical experiment. The prediction model of PQSU was derived by multiple linear regression analysis of the experimental data for conventional image quality metrics and the proposed evaluation metric. The results show that the positive correlation between color harmony and color naturalness is very high. The variation of PQSU can be predicted effectively by color harmony and sharpness. In the three image categories, the proportional coefficients in prediction models for PQSU are different; whereas, the basic forms of prediction models are unchanged. The proposed comprehensive evaluation metric and its prediction model provide a foundation for further developing objective quality evaluation of color fusion images.