The existing general no-reference image quality assessment methods mostly use machine learning method to learn regression models from training images with associated human subjective scores to predict the perceptual quality of testing image. However, such opinion-aware methods expend much time on training, and rely on the distortion types of the training database. These methods have weak generalization capability, hereby limiting their usability in practice. To solve the database dependence, a normalized scale invariance based no-reference image quality assessment method was proposed. In the proposed method, the Natural Scene Statistic (NSS) feature and edge characteristic were combined as the valid features for image quality assessment, and no extra information was required beyond the testing image, then the two feature vectors were used to compute the global difference across scales as the image quality score. The experimental results show that the proposed method has good evaluation for multi-distorted images with low computational complexity. Compared to the state-of-the-art no-reference image quality assessment models, the proposed method has better comprehensive performance, and it is suitable for applications.

Video key frame reflects the main content of the video sequence. Video key frame extraction is one of the key steps for video content retrieval. Although there are some effective key frame extraction algorithms, these algorithms still have some problems such as heavy load of computing, difficulty in choosing suitable threshold value for different type sequences and limited types of videos. In this paper, a video key frame extraction method based on frame dominant color was proposed. Firstly, every frame was simplified by the dominant color which was obtained by octree structure color quantization algorithm. Secondly, shot boundary was detected according to the color similarity between adjacent frames. Finally, key frames were decided from candidate frames by K-means clustering algorithm. The experimental results show that the proposed method is simpler in computation and requires lower time and space complexity than other key frame extraction methods.