DeepSIC: Deep Semantic Image Compression
This work addresses the need for efficient image processing in applications like object recognition, though it appears incremental as it builds on existing compression and semantic analysis methods.
The paper tackles the problem of redundant semantic analysis in image compression by proposing DeepSIC, which integrates semantic information into codecs to reduce computation in client-side applications, achieving promising results on benchmarking datasets.
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 architectures, the feature maps are shared by the compression and the semantic analytics modules. To validate our approaches, we conduct experiments on the publicly available benchmarking datasets and achieve promising results. We also provide a thorough analysis of the advantages and disadvantages of the proposed technique.