A new underwater image quality assessment method with no-reference and no-handcrafted features is proposed in this study to address the lack of acknowledged methods for evaluating the performance of underwater images and the existing assessment methods with various limitations. The proposed assessment method is based on a deep learning net framework and random forest regression model. The very deep convolutional neural network is first used to extract image features. The extracted features and labeled underwater image data set are then employed to train the regression model. The trained regression model is finally used to predict the quality of underwater images. The proposed assessment method is tested and compared on the collected and labeled underwater image data set and the results of underwater image sharpness algorithms. The comparisons include the predicted results and subjective scores

the results of underwater image sharpness algorithms

the correlation between the predicted results and subjective scores

and robustness. Qualitative experiments demonstrate that the proposed method can relatively accurately output the image quality scores in accordance with human visual perception and has better robustness. Quantitative experiments demonstrate that the proposed method has higher correlation with the subjective quality scores when compared with several image quality assessment methods. A new method for assessing the quality of underwater images is proposed. The reference image and handcrafted features are no longer required by utilizing the learning and representation ability of the deep learning net framework. The proposed assessment method is accurate

robust

and general. Moreover

the predicted quality scores are similar to the perception of the human visual system. The proposed method is suitable for original underwater images and the results of underwater image sharpness algorithms.