Compressed Domain Video Abstraction Based on I-Frame of HEVC Coded Videos

摘要

Video abstraction allows indexing, searching, browsing and evaluating a video only by accessing its useful contents. Several studies have been done in this field, but most of them are in pixel domain and require decoding process. It makes these methods more time and process consuming than compressed domain video abstraction. In this paper, we present a new video abstraction method in H.265/HEVC compressed domain, HVAIF. The method is based on the normalized histogram of extracted I-frame prediction modes from an H.265/HEVC coded video. The frames' similarity is calculated by intersecting their I-frame prediction modes' histogram. The similarity measure detects and removes redundant key-frames to increase the quality of final video abstraction. Moreover, we employ fuzzy c-means clustering to categorize similar frames and extract key-frames as representatives of the entire video frames. The interpretation of the results shows that using the proposed method achieves on average 86% accuracy and 19% error rate in compressed domain video abstraction which is higher than the other tested methods in the pixel domain. Also, it has an acceptable robustness to coding parameters, and on average it generates video key-frames that are closer to human summaries.