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Multidimensional Signal Processing
Published in Richard C. Dorf, Circuits, Signals, and Speech and Image Processing, 2018
Yun Q. Shi, Wei Su, Chih-Ming Chen, Sarah A. Rajala, N.K. Bose, L.H. Sibul
Today video coding standards exist for a range of video applications. H.261 was adopted by ITU as a standard for ISDN video teleconferencing in 1990. About five years later ITU adopted H.263, a standard that enables video communications over analog telephone lines and in more recent phases video communication among desktop and mobile terminals connected to the Internet. ISO developed the MPEG standards. MPEG-1 was developed for progressively scanned video used in multimedia applications and includes standardization for both audio and video. MPEG-2 was developed as a standard for TV and HDTV. The goal was to have MPEG-2 systems somewhat compatible with MPEG-1, have error resilience, allow transmission over ATM networks, and transport more than one TV program in a stream. With the evolution of highly interactive multimedia applications, the need arose for a new standard. These applications not only require efficient compression, but also scalability of contents, interactivity with individual objects, and a high degree of error resilience. MPEG-4 was developed to allow for object-based coding of natural and synthetic audio and video, along with graphics. Recent efforts are focused on the development of MPEG-7, a set of tools to facilitate finding the content you need in multimedia data.
Media Systems Integration
Published in Al Kovalick, Video Systems in an IT Environment, 2013
MPEG7 is an established metadata standard for classifying various types of multimedia information. Despite its name, MPEG7 is not an A/V encoding standard such as MPEG4. MPEG7 is formally called a “Multimedia Content Description Interface.” For an overview of MPEG-7 technologies, see (Hasegawa 2004). The standard supports a wide range of metadata features from video characteristics such as shape, size, color, and audio attributes such as tempo, mood, and key to descriptive elements such as who, what, when, and where. MPEG7 has found little use in professional A/V production so far. However, it has found application by the TV-Anytime Forum (personal video recorder products). Their defined metadata specification and XML schema are based on MPEG7’s description definition language and its description schemas.
Emerging Multimedia Standards
Published in Ling Guan, Yifeng He, Sun-Yuan Kung, Multimedia Image and Video Processing, 2012
MPEG-7 is not a coding standard and it is the standard of Multimedia Content Description Interface, which is an ISO/IEC standard developed by MPEG [6]. The objectives of MPEG-7 include: Describe main issues about the content (low-level characteristics, structure, models, collections, etc.).Index a big range of applications.Audiovisual information that MPEG-7 deals with are audio, voice, video, images, graphs, and three-dimensional (3D) models.Inform about how objects are combined in a scene.Independence between description and the information itself.
Localization strategies for robotic endoscopic capsules: a review
Published in Expert Review of Medical Devices, 2019
Federico Bianchi, Antonino Masaracchia, Erfan Shojaei Barjuei, Arianna Menciassi, Alberto Arezzo, Anastasios Koulaouzidis, Danail Stoyanov, Paolo Dario, Gastone Ciuti
Initially, image analysis for GI pathologies identification and capsule pose detection was entirely performed by physicians. In order to: (i) automatize the analysis; (ii) reduce the processing time; and (iii) perform a pathology detection not dependent by physician’s skills (currently of interest for commercial applications), several image classifiers based on Artificial Neural Network (ANN), Vector Quantization (VQ), Support Vector Machines (SVM) and so on, were investigated. In [72] Duda et al. conducted a performance analysis, comparing ANN, VQ and VQ in addiction to Principal Component Analysis (PCA) classification algorithms. In this study, the authors used the homogeneous texture of the MPEG-7 coded multimedia as content descriptor [73]. By analyzing images obtained from a WCE diagnosis of the GI tract (from esophagus to duodenal cap), authors showed how ANN-based classifier better outperforms than VQ-based, reaching an 85% of success in recognition. Furthermore, authors also showed how the introduction of PCA considerably reduces the computational speed but maintains the same performances.
VidOnt: a core reference ontology for reasoning over video scenes*
Published in Journal of Information and Telecommunication, 2018
The majority of upper and domain ontologies introduced for representing multimedia contents in the last decade and a half, with or without MPEG-7 alignment, lack complex role inclusion axioms and rule-based definitions of common video events, and are limited to highly specific terminological and assertional knowledge. For this reason, most of these formalisms are actually controlled vocabularies or taxonomies only, and not fully featured ontologies, hence they are not suitable for advanced multimedia reasoning. To address the above issues, concepts, roles, individuals, and relationships of professional video production, video timeline structure, and common video events have been formally modeled using , one of the most expressive decidable DLs, and implemented in OWL 2. The resulting ontology, the Video Ontology (VidOnt), features a vocabulary that has been aligned with standards such as MPEG-7, EBU CCDM, and Dublin Core, and defines concepts in a novel taxonomical structure. This helps the implementation of de facto standard ontologies for video scene interpretation, and provides core terms not defined by any other multimedia ontology. To push the expressivity boundaries of its logical underpinning, the Video Ontology employs rule-based formalisms, while taking into account the importance of keeping reasoning complexity to a minimum.
FEED2SEARCH: a framework for hybrid-molecule based semantic search
Published in International Journal of General Systems, 2023
Nathalie Charbel, Christian Sallaberry, Sebastien Laborie, Richard Chbeir
We evaluate these existing standards and data models based on Challenge 1 detailed in Section 1. The results are depicted in Table 1. In general, there is no existing standard or data model capable of fully addressing this challenge. Single media-based standards, such as the Exchangeable Image File (EXIF 2002) for describing digital images and the Text Encoding Initiative (TEI 1994) for describing information and meta-information within a textual document, are limited since they mainly handle one type of media. However, (TEI 1994) can be reused in our proposal since it provides relevant structural metadata on the text, which is not covered by multimedia-based standards (DC 2012; MPEG-7 2001). Although the Dublin Core (DC 2012) and the Multimedia Content Description Interface standard (Mpeg-7 2001) handle very well general metadata and content description of multimedia documents respectively, they partially describe document dependencies. For instance, DC excludes intra-document relations and complex inter-document relations (e.g. a spatial relation between different resources). Ontologies and knowledge-based models are in general robust solutions for handling advanced reasoning capabilities and extensibility issues, which is not covered by other data models whether describing multimedia metadata (e.g. Brut et al. 2009) or building related documents (e.g. CSTB 2018; Newforma 2018) since these models are build on traditional ways of representing the data, neglecting semantic information. Nonetheless, the reviewed ontologies and knowledge-based models partially cover these two challenges. For instance, the ontology and knowledge based models, whether representing multimedia document's content (e.g. Arndt et al. 2007; Garcia and Celma 2005; Ontotext 2014; Saathoff and Scherp 2010; W3C 2012) or the building data (e.g. Pauwels and Terkaj 2018; Pauwels, Holten Rasmussen, and Georg Ferdinand 2018; Rasmussen et al. 2018; Rasmussen 2018), partially integrate some advanced semantic aspects which are limited to reasoning capabilities over the content of the documents. Hence, their extensibility follows narrow directions since they are limited to their own coverage (e.g. audiovisual information, building data).