Arithmetic coding for data compression
Communications of the ACM
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Data Compression: The Complete Reference
Data Compression: The Complete Reference
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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Onboard image compression systems reduce the data storage and downlink bandwidth requirements in space missions. This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. Prediction-based compression systems, such as DPCM and JPEG-LS, and transform-based compression systems, such as CCSDS-IDC and JPEGXR, were tested over twenty multispectral (5-band) images from CCD optical sensor of the CBERS-2B satellite. Performance evaluation of these algorithms was conducted using both quantitative rate-distortion measurements and subjective image quality analysis. The PSNR, MSSIM, and compression ratio results plotted in charts and the SSIM maps are used for comparison of quantitative performance. Broadly speaking, the lossless JPEG-LS outperforms other lossless compression schemes, and, for lossy compression, JPEG-XR can provide lower bit rate and better tradeoff between compression ratio and image quality.