An image and video quality assessment method was developed using neural network and support vector machines (SVM) with the peak signal to noise ratio (PSNR) and the structure similarity indexes used to describe image quality. The neural network was used to obtain the mapping functions between the objec-tive quality assessment indexes and subjective quality assessment. The SVM was used to classify the im-ages into different types which were accessed using different mapping functions. Video quality was as-sessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. Simulation results show that the method accurately accesses image quality. The monotonicity of the method for images is 6.94% higher than with the PSNR method,and the root mean square error is at least 35.90% higher than with the PSNR. An image and video quality assessment method was developed using neural network and support vector machines (SVM) with the peak signal to noise ratio (PSNR) and the structure similarity indicators used to describe image quality. The neural network was used to obtain the mapping functions between the objec-tive quality assessment indexes and subjective quality assessment. The SVM was used to classify the im-ages into different types which were accessed using different mapping functions. Video quality was as-sessed based on the quality of each frame in the video sequence with various weights to describe motion and scene changes in the video. The number of isolated points in the correlations of the image and video subjective and objective quality assessments was reduced by this method. monotonicity of the method for images is 6.94% higher than with the PSNR method, and the root mean square error is at least 35.90% higher than with the PSNR.