DeepSIC: Deep Semantic Image Compression
Sihui Luo, Yezhou Yang, Mingli Song

TL;DR
DeepSIC introduces two innovative architectures for image compression that embed semantic information during encoding or decoding, reducing redundant analysis and enabling semantic-aware image storage and transmission.
Contribution
The paper proposes two novel architectures for semantic image compression that integrate semantic analysis into the compression process, enhancing efficiency and semantic preservation.
Findings
Achieved promising results on benchmarking datasets.
Shared feature maps improve compression and semantic analysis integration.
Analyzed advantages and disadvantages of the proposed methods.
Abstract
Incorporating semantic information into the codecs during image compression can significantly reduce the repetitive computation of fundamental semantic analysis (such as object recognition) in client-side applications. The same practice also enable the compressed code to carry the image semantic information during storage and transmission. In this paper, we propose a concept called Deep Semantic Image Compression (DeepSIC) and put forward two novel architectures that aim to reconstruct the compressed image and generate corresponding semantic representations at the same time. The first architecture performs semantic analysis in the encoding process by reserving a portion of the bits from the compressed code to store the semantic representations. The second performs semantic analysis in the decoding step with the feature maps that are embedded in the compressed code. In both…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
