An algorithmic study on lossless image compression

Wu, Xiaolin

doi:10.1109/dcc.1996.488320

Cited by 38 publications

(2 citation statements)

References 14 publications

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“…JPEG-LS [28][29], an improved version of lossless JPEG that was developed based on LOCO-I (Low Complexity Lossless Compression for Images), provides more compression than lossless JPEG because JPEG-LS can perform a good decorrelation. The context-based, adaptive, lossless image codec (CALIC) [30], a gradient-adjusted predictor-based compression technique, gives more compression than JPEG-LS. PNG [15][16][17], a patent-free replacement for the GIF, is a lossless image compression method: it compresses an image using a prediction [18] and an entropy coding technique [19].…”

Section: Background Studiesmentioning

confidence: 99%

A comparative analysis of the state-of-the-art lossless image compression techniques

2022

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Lossless data reduction is essential for data transmission over the Internet and the storage of data in a digital device when data loss is not permitted. The application of image compression is essential for image storing, image classification, and image recognition, and image compression techniques compress an image by reducing redundancy in the image. Many image compression standards have already been developed. This article compares the most popular state-of-the-art lossless image compression techniques, and the methods are evaluated based on the bits per pixel or compression ratio. Finally, we recommend which of the algorithms is better for a few different datasets.

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Section: Background Studiesmentioning

confidence: 99%

A comparative analysis of the state-of-the-art lossless image compression techniques

2022

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“…Recently, many lossless schemes for image compression [1][2][3][4] based on arithmetic coding ͑AC͒ 5,6 have been proposed. The goal of these schemes is to represent an image with the smallest possible number of bits without loss of any information in order to speed up transmission and minimize storage requirements.…”

Section: Introductionmentioning

confidence: 99%

Efficient adaptive arithmetic coding based on updated probability distribution for lossless image compression

Masmoudi

Puech

Bouhlel

2010

J. Electron. Imaging

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We propose an efficient lossless compression scheme for still images based on arithmetic coding. The scheme presents a novel adaptive arithmetic coding that updates the probabilities of pixels only after detecting the last occurrence of each pixel and then removes the redundancy from the original image effectively. The proposed approach has interestingly low computational complexity. In addition, unlike other statistical coding techniques, arithmetic coding in the proposed scheme is not solely dependent on the pixel probability distribution but also on the image block sorting. The proposed method is compared to both static and adaptive order-0 models while taking into account compression ratios and processing time. Experimental results, based on a set of 100 gray-level images, demonstrate that the proposed scheme gives mean compression ratios that are 5.5% higher than those by the conventional arithmetic encoders as well as significantly faster than the order-0 adaptive arithmetic coding.

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