Distortion function for distributed deep image compression

Yujie Song; Shengping Wu; Dexin Li; Qiao Huang

doi:10.1117/12.3034429

16 October 2024 Distortion function for distributed deep image compression

Yujie Song, Shengping Wu, Dexin Li, Qiao Huang

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329167 (2024) https://doi.org/10.1117/12.3034429
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Distributed Deep Image Compression (DDIC) is an emerging technique that utilizes inter-image correlations within the decoder to achieve efficient transmission and storage. The choice of distortion function plays a crucial role in the rate-distortion performance of DDIC, yet existing work lacks in-depth research specifically focused on distortion functions for DDIC. In order to further enhance the rate-distortion performance of DDIC, we propose a novel hybrid distortion function that employs a weighted combination of structural-level and pixel-level distortions, aiming to enhance the preserved structural features of compressed images while considering pixel-level characteristics. During the training process, the robustness and flexibility of the model are improved by balancing the attention on the dual features. The proposed hybrid loss demonstrates superior performance compared to other approaches on the KITTI and Cityscapes datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yujie Song, Shengping Wu, Dexin Li, and Qiao Huang "Distortion function for distributed deep image compression", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329167 (16 October 2024); https://doi.org/10.1117/12.3034429

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