Abstract: Due to the absorption and scattering effects under water,underwater image often suffers from blurring,low contrast,color casting and so on.The degraded images will decline the accuracy and effectiveness in underwater archaeology,marine ecological research,underwater target detection and tracking.On the other hand,underwater image quality assessment plays a key goal in the development and exploration of the ocean.An effective underwater image quality evaluation system can provide a gui-dance for optimizing underwater enhancement and restoration algorithms and promote the progress of underwater vision.Therefore,it is desire to design an effective and robust algorithm for underwater image quality evaluation.Since the atmospheric image quality evaluation methods don’t consider the characteristics of the water absorption of light,they aren’t suitable for evaluating underwater image quality.Additionally,there are few effective metrics for underwater images quality evaluation up to now.To address this problem,we propose a new no-reference underwater image quality measure containing color index,contrast,and sharpness indexes,dubbed CCS,which has stronger correlation with human subjective perception.These attributes not only are sensitive to the physical characteristics of the water,but also the human visual system (HVS) is sensitive to the changes of the visual properties such as color,contrast,and edge structures.To verify the performance of the proposed CCS,we conduct considerable experiments on a small underwater image dataset comparing with the other four non-reference metrics including CPBD,BRISQUE,UIQM and UCIQE.It can be seen that our CCS metric is higher about 13% than UIQM in terms of RMSE,moreover,is higher above 10% than UIQM and UCIQE in terms of PLCC,SROCC,and KROCC.Experimental results demonstrate that the proposed CCS metric has a higher correlation with subjective evaluations,which can effectively and accurately evaluate the underwater image quality.

Key words: Colorful measure, Contrast measure, Human visual system, Image quality assessment, No-reference, Sharpness measure