Experimental study on JPEG-LS algorithm

Amashi, Radhika; Baligar, Vishwanath P.; Kalwad, Priyadarshini D.

doi:10.1109/icpcsi.2017.8391965

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…p0 = 1/4,p1 = 3/4. the expression of the symbol "0" is [0, 1) and the expression of the symbol "1" is [1,4).…”

Section: Ans Codementioning

confidence: 99%

“…JPEG-LS provides superior compression performance for pictures with a high amount of image information redundancy, making it a good choice for image compression [2][3][4]. The compression performance of JPEG-LS, however, is progressively unable to fulfil the requirement for large-format remote sensing satellite data with copious amounts of information and rising camera frame frequency.…”

Section: Introductionmentioning

confidence: 99%

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A lossless compression of remote sensing images based on ANS entropy coding algorithm

Wang,

Zhang

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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Large-format, information-dense satellite-based remote sensing pictures are in conflict with the restricted bandwidth for return transmission to the ground, necessitating the development of appropriate remote sensing image compression techniques. With regard to the data transmission requirements of large-format remote sensing photos, we investigate common lossless compression techniques in this study and suggest a superior approach based on ANS entropy coding. This algorithm, which is superior to Golomb entropy coding in JPEG-LS image compression algorithm in terms of simplicity, high coding efficiency, and extremely low post-compression coding redundancy, can be used to process remote sensing image entropy coding and significantly boost the performance of remote sensing image compression. Research demonstrates that when compared to the original coding scheme, the approach increases the compression ratio and efficiency, decreases the amount of data that must be downlinked, and increases transmission speed. The requirements for real-time processing of large-format remote sensing images can be met by compression ratios that are very close to the limit of lossless compression for remote sensing satellite images with less information and uniform pixel value distribution. These images also have a lower algorithm complexity and require less computing time.

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“…p0 = 1/4,p1 = 3/4. the expression of the symbol "0" is [0, 1) and the expression of the symbol "1" is [1,4).…”

Section: Ans Codementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%