A critical evaluation of image and video indexing techniques in the compressed domain

Compressed Domain Scene Change Detection Based on Transform Units Distribution in High Efficiency Video Coding Standard

Scene change detection plays an important role in a number of video applications, including video indexing, searching, browsing, semantic features extraction, and, in general, pre-processing and post-processing operations. Several scene change detection methods have been proposed in different coding standards. Most of them use fixed thresholds for the similarity metrics to determine if there wa...

Image and Video Indexing in the Compressed Domain: A Critical Review

Image and video indexing techniques are crucial in multimedia applications. A number of the indexing techniques that operate in the pixel domain have been reported in the literature. The advent of compression standards has led to the proliferation of indexing techniques in the compressed domain. In this paper, we present a critical review of the compressed domain indexing techniques proposed in...

Video Abstraction in H.264/AVC Compressed Domain

Video abstraction allows searching, browsing and evaluating videos only by accessing the useful contents. Most of the studies are using pixel domain, which requires the decoding process and needs more time and process consuming than compressed domain video abstraction. In this paper, we present a new video abstraction method in H.264/AVC compressed domain, AVAIF. The method is based on the norm...

Tools for Compressed-Domain Video Indexing and Editing

Indexing and editing digital video directly in the compressed domain offer many advantages in terms of storage efficiency and processing speed. We have designed automatic tools in the compressed domain for extracting key visual features such as scene cut, dissolve, camera operations (zoom, pan), and moving object detection and tracking. In addition, we have developed algorithms to solve the dec...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models