SAR Image Compression Using Discretized Gaussian Adaptive Model and Generalized Subtractive Normalization

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

“…Building on Xu's foundation, Zhang et al further refined the model by introducing a hybrid Gaussian model for fitting and estimating model parameters. This modification, validated on ICEYE and Sandia datasets, demonstrated superiority over traditional compression methods and learning-based algorithms [37]. In a separate contribution [38], a compression algorithm featuring pyramid features and quality enhancement was proposed.…”

“…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 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.…”