Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. We reviewed a total of 101 papers published from 2000 to 2021. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. Subjective comparisons also corroborated with the objective metrics in that perceptually lossless compression can be achieved even at 20 to 1 compression. In particular, the performance gain of the SB approach with Daala is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG. We observed that perceptually lossless compression can be achieved at 10:1 compression ratio. Extensive experiments using actual Mastcam images have been performed to demon-strate the various approaches. Two are conven-tional and another two are known to have good correlation with human perception. The performance of different al-gorithms was assessed using four well-known performance metrics. Five well-known compression co-decs, including JPEG, JPEG-2000 (J2K), X264, X265, and Daala in the literature, have been applied and compared in each approach. In addition, we also present subjective and ob-jective assessment results for compressing RGB images because RGB images have been used for stereo and disparity map generation. The third one is to apply a two-step approach in which the first step uses principal component analysis (PCA) to compress a nine-band image cube to six bands and a second step compresses the six PCA bands using conventional codecs. The second approach is to compress each group separately and we call it the split band (SB) approach. We call this approach the Video approach. Since the multispectral bands have strong correlation, we treat the three groups of images as video frames. The first approach is to divide the nine bands into three groups with each group hav-ing three bands. We present a comparative study of four approaches to compressing multispectral Mastcam images. Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times of compression. The two mast cameras, Mastcams, onboard Mars rover Curiosity are multispectral imagers with nine bands in each.
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