Information Technology Reference
In-Depth Information
Chapter 22
Scalable Indexing of HD Video
Jenny Benois-Pineau, Sandrine Anthoine, Claire Morand,
Jean-Philippe Domenger, Eric Debreuve, Wafa Bel Haj Ali, and Paulo Piro
Abstract.
HD video content represents a tremendous quantity of information that all
types of devices can not easily handle. Hence the scalability issues in its processing
have become a focus of interest in HD video coding technologies. In this chapter, we
focus on the natural scalability of hierarchical transforms to tackle video indexing
and retrieval. In the first part of the chapter, we give an overview of the transforms
used and then present the methods which aim at exploring the transform coefficients
to extract meaningful features from video and embed metadata in the scalable code-
stream. Statistical global object-based descriptor incorporating low frequency and
high-frequency features is proposed. In the second part of the chapter, we introduce
a video retrieval technique based on a multiscale description of the video content.
Both spatial and temporal scalable descriptors are proposed on the basis of multi-
scale patches. A statistical dissimilarity between videos is derived using Kullback-
Leibler divergences to compare patch descriptors.
1
Introduction
HD video content represents a tremendous quantity of information that cannot be
handled by current devices without adapting the processing chain. There is thus a
need to develop new content-based indexing methods adapted to 1) the high qual-
ity and complexity of the HD video content and 2) the fact that such a content
will be accessed through heterogeneous networks. In particular, scalability is a most
