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Multi-Task Convolution Neural Network-Based Lifting Scheme for Image Compression

Pattern recognition letters, 2024 [Peer Reviewed Journal]

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Title:
Multi-Task Convolution Neural Network-Based Lifting Scheme for Image Compression
Author: Dardouri, Tassnim ; Kaaniche, Mounir ; Benazza-Benyahia, Amel ; Dauphin, Gabriel
Subjects: Engineering Sciences
Is Part Of: Pattern recognition letters, 2024
Description: Lifting schemes have attracted much interest in different image processing tasks, and more specifically in the image compression field. In this context, the optimization of the lifting operators (i.e. the prediction and update ones) plays a crucial role in the design of efficient lifting-based image coding systems. In this respect, we propose in this paper to further investigate the exploitation of neural networks in a standard non-separable lifting scheme structure. More precisely, unlike previous works, where different neural network models are employed for all the prediction and update steps involved in a lifting scheme-based decomposition, our design consists in building a new multi-task convolutional neural network model that takes into account the similarities between two prediction stages. Simulations carried out on three popular image datasets show the benefits of the proposed learning-based image coding approach.
Publisher: Elsevier
Language: English
Identifier: ISSN: 0167-8655
EISSN: 1872-7344
Source: Hyper Article en Ligne (HAL) (Open Access)

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