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- W4361764064 abstract "In view of the fact that multispectral image has extensive application prospects and contains rich information, how to extract features efficiently is crucial for multispectral image compression. This paper proposed a new compressed framework which utilizes ConvGRU and contextual transformer respectively as the backbone of the spectral attention and Cot block to capture the spectral and spatial features. Additionally, residual units are used to explore deeper features to reserve fine details of image. The image data is firstly fed into the encoder which is composed of aforementioned units and then the quantizer and entropy encoder to convert data into the bit stream. The rate distortion optimization is utilized to perfect the reconstructed image better. Our method is compared with JPEG2000 via PSNR. The experimental results demonstrate that the method outperforms JPEG2000." @default.
- W4361764064 created "2023-04-04" @default.
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- W4361764064 date "2023-01-01" @default.
- W4361764064 modified "2023-09-30" @default.
- W4361764064 title "Multispectral Image Compression Based on Improved Residual Network" @default.
- W4361764064 cites W2963149687 @default.
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- W4361764064 doi "https://doi.org/10.1007/978-981-99-1260-5_29" @default.
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