Abstract: Before digital images become available to consumers, they usually undergo several stages of processing, which include acquisition, compression, transmission, and presentation. Unfortunately, each stage introduces certain types of distortion, such as white noise, Gaussian blur, and compression distortion, which may degrade the perceptual quality of the final image. Therefore, it is important to design an effective and robust image quality assessment method to automatically evaluate distortions in image quality. Image quality assessment is widely used in image compression, image deblur, image enhancement, and other image processing domains. In general, no-reference image quality assessment methods have profound practical significance and broad application value; hence, it remains the main focus of many researchers. At present, many image quality assessment methods extract features and predict image quality using single edge detection operations such as gradient or local binary pattern. However, it is difficult for a single edge detection operation to represent the whole perceptual quality of distorted images, and hence, their predictions may not be satisfactory. To eliminate the limitations of single edge detection operation, this paper proposes a new no-reference image quality assessment method based on a multiple edge detection operation. The paper considers first-order and second-order derivative information and utilize their similarity to predict image quality. The proposed method first converted color images to grayscale images, and calculated the gradient magnitude (GM), relative gradient magnitude (RM), relative gradient orientation (RO), and Laplacian of Gaussian (LOG) of the grayscale images. The feature vectors extracted from the maps were divided into two parts, where one part was the standard deviation of RO, and the second part utilized conditional entropy to quantify the similarity and relationship of GM, RM, and LOG. The images were naturally multiscale, and distortions affected the image structures across scales. Hence, all features at two scales were extracted:the original image scale and at a reduced resolution (low-pass filtered and down sampled by a factor of 2). Lastly, an AdaBoost back-propagation network was used to train and establish a regression model to predict the image quality. The experiment of the proposed method was performed on two public databases, LIVE and TID2008, and the results show that the score predicted by this new method has a good correlation with the subjective quality score. Moreover, this method can reflect perceptual quality properly using only ten-dimensional feature vectors, and the performance of correlation coefficient can exceed some state-of-the-art no-reference image quality assessment algorithms.