Fast intra mode decision algorithm for H.263 to H.264/AVC transcoding

2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

De l'estimation de mouvement pour l'analyse temps réel de vidéos dans le domaine compressé

Analyser des vidéos directement dans le domaine compressé nécessite de disposer de procédures précises d'estimation des vecteurs de mouvement. Notre contribution porte sur la mise au point d'une procédure temps réel de traitement de ces vecteurs faisant suite à une analyse statistique de leur répartition, et aboutissant aux solutions de filtrage adéquates. Les nouveaux algorithmes ont été implantés et leur apport dans le cadre d'un corpus de séquences de vidéo-surveillance démontré avec une accélération du temps de calcul d'un facteur cinq par rapport aux performances décrites dans la littérature

Adaptation des images et des vidéos pour des utilisateurs multiples dans des environnements hétérogènes

La dernière décennie a connu l'émergence de l'utilisation des équipements mobiles comme les assistants personnels et les téléphones, ainsi que la prolifération des réseaux personnels favorisée par le développement considérable dans les technologies de communications. D'autre part, l'information véhiculée a travers le World Wide Web devient de plus en plus visuelle (images et videos) grâce à la numérisation. Afin de permettre à tous les usagers un accès universel à cette information visuelle dans un environnement caractérisé par la diversité des équipements et l'hétérogénéité des réseaux, il devient nécessaire d'adapter les documents multimédia. L'adaptation consiste à appliquer une ou plusieurs transformations sur un document multimédia. Dans ce cadre, plusieurs travaux ont été élaborés en partant de différentes formulations. Nous pensons qu'un système d'adaptation efficace doit choisir les traitements nécessaires à appliquer sur un document visuel afin de maximiser la satisfaction de l'usager. Il doit considérer conjointement les caractéristiques de cet usager ainsi que les performances de son équipement, la qualité de sa connexion et les conditions de son environnement. La majorité des travaux réalisés dans ce domaine n'ont traité que des cas limités, par exemple ajuster une vidéo pour la capacité d'un réseau donné. Dans la présente recherche, nous proposons une solution globale obtenue à l'aide d'un modèle probabiliste qui utilise les traitements des images et des vidéos et l'extraction des caractéristiques des contenus

Adaptive Edge-Oriented Shot Boundary Detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included

Mapping Stream Programs into the Compressed Domain

Due to the high data rates involved in audio, video, and signalprocessing applications, it is imperative to compress the data todecrease the amount of storage used. Unfortunately, this implies thatany program operating on the data needs to be wrapped by adecompression and re-compression stage. Re-compression can incursignificant computational overhead, while decompression swamps theapplication with the original volume of data.In this paper, we present a program transformation that greatlyaccelerates the processing of compressible data. Given a program thatoperates on uncompressed data, we output an equivalent program thatoperates directly on the compressed format. Our transformationapplies to stream programs, a restricted but useful class ofapplications with regular communication and computation patterns. Ourformulation is based on LZ77, a lossless compression algorithm that isutilized by ZIP and fully encapsulates common formats such as AppleAnimation, Microsoft RLE, and Targa.We implemented a simple subset of our techniques in the StreamItcompiler, which emits executable plugins for two popular video editingtools: MEncoder and Blender. For common operations such as coloradjustment and video compositing, mapping into the compressed domainoffers a speedup roughly proportional to the overall compressionratio. For our benchmark suite of 12 videos in Apple Animationformat, speedups range from 1.1x to 471x, with a median of 15x

Astronomical image manipulation in the transform domain

It is well known that images are usually stored and transmitted in the compressed form to save memory space and I/O bandwidth. Among many image compression schemes, transform coding is a widely used coding method. Traditionally, processing a compressed image requires decompression first. Following manipulations, the processed image is compressed again for storage. To reduce the computational complexity and processing time, manipulating images in the semi-compressed or transform domain is an efficient solution; Many astronomical images are compressed and stored by JPEG and HCOM-PRESS, which are based on the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT), respectively. In this thesis, a suite of image processing algorithms in the transform domain, DCT and DWT, is developed. In particular, new methods for edge enhancement and minimum (MIN)/maximum (MAX) gray scale intensity estimation in the DCT domain are proposed. Algebraic operations and image interpolation in the DWT domain are addressed. The superiority of new algorithms over the conventional ones is demonstrated by comparing the time complexities and qualities of the processed image in the transform domain to those in the spatial domain

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

System for caption text extraction on a hierarchical region-based image representation

English: This work presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects