Video Compression Using Conditional Replenishment and Motion Prediction

Hein, D. N.; Ahmed, N.

doi:10.1109/temc.1984.304204

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…3, more than 85% of the pixels have no significant changes. Therefore, difference coding 34 in MPEG and H.265 series reuses existing frames and updates only the pixels with significant changes.…”

Section: Compressive Video Sensingmentioning

confidence: 99%

Nonconvex compressive video sensing

Chen

Yan

Cui

et al. 2016

J. Electron. Imaging

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High-speed cameras explore more details than normal cameras in the time sequence, while the conventional video sampling suffers from the trade-off between temporal and spatial resolutions due to the sensor’s physical limitation. Compressive sensing overcomes this obstacle by combining the sampling and compression procedures together. A single-pixel-based real-time video acquisition is proposed to record dynamic scenes, and a fast nonconvex algorithm for the nonconvex sorted ℓ1 regularization is applied to reconstruct frame differences using few numbers of measurements. Then, an edge-detection-based denoising method is employed to reduce the error in the frame difference image. The experimental results show that the proposed algorithm together with the single-pixel imaging system makes compressive video cameras available.

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Section: Compressive Video Sensingmentioning

confidence: 99%

Nonconvex compressive video sensing

Chen

Yan

Cui

et al. 2016

J. Electron. Imaging

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“…The stationary-change model includes simple predictive [68] and conditional replenishment techniques [69], [70]. It assumes the absence of any image flow so that any changes are due only to an "in-place" change of pixel values.…”

Section: Video Interframe Coding Modelsmentioning

confidence: 99%

Hypermedia Data Modeling, Coding, And Semiotics

1997

Proc. IEEE

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This paper reviews the key issues in hypermedia systems as an overture to the proposal of a new semiotic paradigm for hypermedia data and coding models. The hypertext concept permits users to interact with and manage data as high-level conceptual objects rather than as symbol streams. Current hypermedia systems can best be defined as an amalgamation of hypertext and multimedia. While the hypertext data model enables this goal, that is not true for the data models of other media forms. A new semiotic paradigm that addresses these deficiencies and supports object-oriented interaction with compressed multimedia streams is proposed. This paper initially presents an overview of the hypertext data model, contrasting it with existing multimedia data and coding models. The framework for the new paradigm is then presented in a brief review of cognitive, psychological, and semiotic principles. This analysis culminates in the proposal of semiotically based data models and representations predisposed to the hypermedia paradigm.

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