Scalable Motion-Adaptive Video Coding with Redundant Representations

This paper presents a scalable video coding scheme (MP3D), based on the use of a redundant 3-D spatio-temporal dictionary of functions. The spatial component of the dictionary consists of directional and anisotropically scaled functions, which form a rich collection of visual primitives. The temporal component is tuned to capture most of the energy along motion trajectories in the video sequences. The MP3D video coding first finds motion trajectories. It then applies a spatio-temporal decomposition using an adaptive approximation algorithm based on Matching Pursuit (MP). The coefficients and the function parameters are quantized and coded in a progressive fashion, under multiple rate constraints, allowing for adaptive decoding by simple bit stream truncation. The motion fields are losslessly coded and transmitted as side information to the decoder. The multi-resolution structure of the dictionary allows for flexible spatial and temporal resolution adaptation. This scheme is shown to yield comparable rate-distortion performances to state-of-the-art schemes, like H.264 and MPEG-4. It represents a promising alternative for low and medium rate applications, or as a flexible base layer for higher rate video systems

Analysis, Visualization, and Transformation of Audio Signals Using Dictionary-based Methods

date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +0000date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +000

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

A comprehensive study of sparse codes on abnormality detection

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We comprehensively study two types of sparse codes solutions - greedy algorithms and convex L1-norm solutions - and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. Experiments show that combining OMP codes with maximum coordinate detection could achieve state-of-the-art performance on the UCSD dataset [14].Comment: 7 page