High-Order Markov Random Field as Attention Network for High-Resolution Remote-Sensing Image Compression

Chong, Yanwen; Zhai, Liang; Pan, Shaoming

doi:10.1109/tgrs.2021.3075956

Cited by 7 publications

(1 citation statement)

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“…Moreover, the interactive dual attention makes the edge extraction network focus only on relevant boundaries, rather than all edges, resulting in savings in bit rate cost and obtaining a strong structural representation. In [43], the authors design a high-order Markov Random Field as an attention network to achieve good compression performance for high-resolution remote sensing image compression. Additionally, some researchers have also designed learned image compression methods based on attention strategies [44,45].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Compression Based on the Multiple Prior Information

2023

Remote Sensing

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Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000.

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Section: Introductionmentioning

confidence: 99%