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HNR-ISC: Hybrid Neural Representation for Image Set Compression

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  • NETL
  • NSTL
  • IEEE
Image set compression (ISC) refers to compressing the sets of semantically similar images. Traditional ISC methods typically aim to eliminate redundancy among images at either signal or frequency domain, but often struggle to handle complex geometric deformations across different images effectively. Here, we propose a new Hybrid Neural Representation for ISC (HNR-ISC), including an implicit neural representation for Semantically Common content Compression (SCC) and an explicit neural representation for Semantically Unique content Compression (SUC). Specifically, SCC enables the conversion of semantically common contents into a small-and-sweet neural representation, along with embeddings that can be conveyed as a bitstream. SUC is composed of invertible modules for removing intra-image redundancies. The feature level combination from SCC and SUC naturally forms the final image set. Experimental results demonstrate the robustness and generalization capability of HNR-ISC in terms of signal and perceptual quality for reconstruction and accuracy for the downstream analysis task.

Image codingDecodingRedundancyNeural networksTermination of employmentImage reconstructionSemanticsCorrelationVideo codecsUrban areas

Pingping Zhang、Shiqi Wang、Meng Wang、Peilin Chen、Wenhui Wu、Xu Wang、Sam Kwong

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Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China|Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China

Department of School of Data Science, Lingnan University, Hong Kong SAR, China

College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China|Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China

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2025

10.1109/TMM.2024.3521715

IEEE transactions on multimedia

IEEE transactions on multimedia

ISSN：
年,卷(期)：2025.27(1)
  • 56