Enhancing Distributed Source Coding With Encoder-Centric Frequency Adaptation and Spatial Transformation

Current methodologies in distributed source coding have predominantly investigated decoder-focused strategies, emphasizing the alignment and exploitation of side information. This study introduces a paradigm shift by presenting an encoder-centric algorithm that conducts proactive optimization in the...

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Veröffentlicht in:IEEE transactions on multimedia Jg. 27; S. 2582 - 2592
Hauptverfasser: Xu, Hao, Tan, Bin, Chen, Yihao, Hu, Die, Wu, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
affine transformation
Correlation
Decoding
Distributed source coding
Feature extraction
Frequency-domain analysis
frequency-domain filtering
Image coding
Image reconstruction
Information filters
Optimization
Source coding
Transformers
ISSN:1520-9210, 1941-0077
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Zusammenfassung:Current methodologies in distributed source coding have predominantly investigated decoder-focused strategies, emphasizing the alignment and exploitation of side information. This study introduces a paradigm shift by presenting an encoder-centric algorithm that conducts proactive optimization in the frequency domain. This shift is motivated by the current deep learning models' tendency to passively extract high-frequency elements, such as contours and content in the spatial domain at the encoder side, without considering the frequency characteristics of these spatial components. Unlike current trends, the proposed scheme actively selects the essential frequency components directly in the frequency domain by introducing an adaptive self-learning filter, enabling the encoder to discern and retain critical frequency components effectively and precisely. Furthermore, we align the side information in the spatial domain before feature extraction and implement an affine transformation-based alignment strategy to utilize the side information better. By leveraging the shared frequency domain components of the image pairs, the proposed algorithm adeptly learns affine coefficients to accomplish precise spatial alignment. This dual strategy of proactive encoder optimization and decoder alignment via affine transformations is highly efficient, outperforming existing state-of-the-art methods in distributed source coding when tested across two diverse datasets by an average of 0.5 dB in PSNR.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2024.3521700