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Analysis of institutional authors

Berzal, JaAuthor

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January 23, 2023
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Video sequence compression via supervised training on cellular neural networks

Publicated to: International Journal Of Neural Systems. 8 (1): 127-135 - 1997-02-01 8(1), DOI: 10.1142/S012906579700015X

Authors: Rodriguez, L; Zufiria, PJ; Berzal, JA

Affiliations

- Author

Abstract

In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network.The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped.Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed.Finally, some examples with real video sequences illustrate the applicability of the method.

Keywords

AlgorithmsArtifactsFeedbackImage compressionImage enhancementMathematicsNeural networks, computerNeuronsPattern recognition, visualSystems

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal Of Neural Systems, Q3 Agency Scopus (SJR), its regional focus and specialization in , give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-12-16:

  • WoS: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-16:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 11 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Berzal Fernández, José Andrés).