Traditional Transformation Theory Guided Model for Learned Image Compression

• URL: http://arxiv.org/abs/2402.15744v1
• Date: Sat, 24 Feb 2024 06:54:29 GMT
• Title: Traditional Transformation Theory Guided Model for Learned Image Compression
• Authors: Zhiyuan Li, Chenyang Ge, Shun Li
• Abstract summary: We propose ultra lows enhanced invertible encoding network guided by traditional transformation theory. Experiments show that our methods outperforms existing methods in both compression and reconstruction performance.
• Score: 10.914558012458425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance and speed at medium and high bitrates, while research on ultra low bitrates is limited. In this work, we propose a ultra low bitrates enhanced invertible encoding network guided by traditional transformation theory, experiments show that our codec outperforms existing methods in both compression and reconstruction performance. Specifically, we introduce the Block Discrete Cosine Transformation to model the sparsity of features and employ traditional Haar transformation to improve the reconstruction performance of the model without increasing the bitstream cost.

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