首页|Saliency Segmentation Oriented Deep Image Compression With Novel Bit Allocation

Saliency Segmentation Oriented Deep Image Compression With Novel Bit Allocation

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  • NETL
  • NSTL
  • IEEE
Image compression distortion can cause performance degradation of machine analysis tasks, therefore recent years have witnessed fast progress in developing deep image compression methods optimized for machine perception. However, the investigation still lacks for saliency segmentation. First, in this paper we propose a deep compression network increasing local signal fidelity of important image pixels for saliency segmentation, which is different from existing methods utilizing the analysis network loss for backward propagation. By this means, these two types of networks can be decoupled to improve the compatibility of proposed compression method for diverse saliency segmentation networks. Second, pixel-level bit weights are modeled with probability distribution in the proposed bit allocation method. The ascending cosine roll-down (ACRD) function allocates bits to those important pixels, which fits the essence that saliency segmentation can be regarded as the pixel-level bi-classification task. Third, the compression network is trained without the help of saliency segmentation, where latent representations are decomposed into base and enhancement channels. Base channels are retained in the whole image, while enhancement channels are utilized only for important pixels, and therefore more bits can benefit saliency segmentation via enhancement channels. Extensive experimental results demonstrate that the proposed method can save an average of 10.34% bitrate compared with the state-of-the-art deep image compression method, where the rate-accuracy (R-A) performances are evaluated on sixteen downstream saliency segmentation networks with five conventional SOD datasets. The code will be available at: https://openi.pcl.ac.cn/OpenAICoding/SaliencyIC and https://github.com/AkeLiLi/SaliencyIC.

Image codingImage segmentationBit rateObject segmentationDistortionImage reconstructionEntropyTrainingSemanticsDegradation

Yuan Li、Wei Gao、Ge Li、Siwei Ma

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Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China

Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China|Peng Cheng Laboratory, Shenzhen, China

National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China

2025

10.1109/TIP.2024.3504282

IEEE transactions on image processing

IEEE transactions on image processing

ISSN：
年,卷(期)：2025.34(1)
  • 65