Abstract
Recent works on learned image compression (LIC) based on convolutional neural networks (CNNs) have achieved great improvement with superior rate-distortion performance. However, the robustness of LIC has received little investigation. In this paper, we proposes a complex-valued learned image compression model based on complex-valued convolutional neural networks (CVCNNs) to enhance its robustness. Firstly, we design a complex-valued neural image compression framework, which realizes compression with complex-valued feature maps. Secondly, we build a module named modSigmoid to implement a complex-valued nonlinear transform and a split-complex entropy model to compress complex-valued latent. The experiment results show that the proposed model performs comparable compression performance with a large parameter drop. Moreover, we adopt the adversarial attack method to examine robustness, and the proposed model shows better robustness to adversarial input compared with its real-valued counterpart.
C. Luo and Y. Bao—These authors contributed to the work equally and should be regarded as co-first authors.
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Acknowledgment
This research was supported by the National Natural Science Foundation of China (Grant No. 62031013), the Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project (Grant No. 2022ZDJS117), and the project of Peng Cheng Laboratory. The computing resources of Pengcheng Cloudbrain are used in this research.
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Luo, C., Bao, Y., Tan, W., Li, C., Meng, F., Liang, Y. (2024). A Complex-Valued Neural Network Based Robust Image Compression. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_5
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DOI: https://doi.org/10.1007/978-981-99-8549-4_5
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