Remote Sensing Image Compression Based on the Multiple Prior Information

Abstract

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.

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

“…Current research on deep-learning-based SAR image compression mainly involves global compression and reconstruction of the entire image using encoding and decoding networks. However, these methods tend to focus primarily on the overall compression performance and metrics at a global level [36][37][38][39][40][41]. Unfortunately, achieving a higher level of information fidelity for specific local targets remains a challenging task, resulting in redundant information in non-target regions.…”

“…Concurrently, the demand for image compression in remote sensing has witnessed a steady increase. Consequently, numerous researchers have dedicated their efforts to exploring learningbased algorithms for compressing remote sensing images [32][33][34][35][36][37][38][39][40]. These learning-based approaches in remote sensing image compression use extensive sample learning to extract key features that leverage the spatial characteristics of images.…”

“…In the same year, Fu et al proposed a model employing multiple prior networks, combining a CNN-based prior network with one based on transformer modules. This cascade approach, although increasing the complexity, resulted in a thorough exploration of spatial redundancy, achieving favorable experimental results on high-resolution remote sensing image (HRRSI) datasets [40].…”

“…In practical applications, most models often overlook errors at data input [21][22][23][24][25][26][27][28][29][30][31][35][36][37][38][39][40]. Commonly, traditional techniques such as linear stretching are employed for quantization processing [41].…”

“…Regrettably, most preprocessing methods based on conventional approaches result in the loss of crucial input features. Additionally, existing models often concentrate solely on spatial redundancy at both global and local levels, without distinguishing between target and non-target regions [36][37][38][39][40][41]. This lack of consideration may adversely affect the compression performance of the models.…”