Fast and efficient lossless image compression

Howard, Paul G.; Vitter, Jeffrey Scott

doi:10.1109/dcc.1993.253114

Cited by 151 publications

(77 citation statements)

References 10 publications

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“…JPEG-LS [13] is the state-of-the-art standard algorithm for lossless and near-lossless image compression in pixel domain. Experimental results with medical thermographic images in [14] shows that JPEG-LS outperforms other pixel domain codecs like CALIC [15] and FELICS [16] in terms of compression factor and for lossless compression. Unfortunately, when used for lossless compression, pixel domain codecs like JPEG-LS have limited compression factor such as 1.5-2.…”

Section: Review Of Standard Image Compression Schemesmentioning

confidence: 99%

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

Wang¹,

Najmabadi²,

Baroud³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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The high temporal and spatial resolution of industrial thermographic cameras creates a rapid growth in the bandwidth of measurement data, such that the bandwidth of widely used standard data transmission links is becoming a performance bottleneck for thermographic systems. This paper proposes the use of real-time compression for the measurement data in future high-performance thermographic cameras. Real-time compression of high-throughput input stream is achieved via a lightweight and low-latency algorithm based on predictive coding. To obtain a high compression ratio while ensuring the quality of compressed data, the compression core exploits image-dependent measurement information such as calibration range and controls the compression quality correspondingly. Experimental results show that the proposed camera integrated thermographic data compression reduces the average required bandwidth by over 85%.

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Section: Review Of Standard Image Compression Schemesmentioning

confidence: 99%

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

Wang¹,

Najmabadi²,

Baroud³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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show abstract

“…Next step is entropy coding, which involves arranging the image components in a 'zigzag' order employing run-length encoding (RLE) algorithm that groups similar frequencies together, inserting length coding zeros, and then using Huffman coding [8] on what is left. In N 8x8 blocks, if the i th block is represented by Bi and positions within each block are represented by p, q where p = 0,1, … ,7 and q = 0,1, … ,7 , then any coefficient in the DCT image can be represented as B i p, q .…”

Section: Next Is Dividing Each Component In 8x8 Blockmentioning

confidence: 99%

Embedded and Flexible Image File Version Control System for Managing Multiple Minor Changes

Virparia¹,

Patel²,

Patel³

2017

IJCA

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With the increasing use of image as a source of information, there arises a need to efficiently process such images that results in reduced user effort as well as the storage space. This research project focuses on a way to implement version control mechanism on image data. This mechanism provides a feature where a/the user can alter any small changes in the data without modifying the original image data. A footer is formed which includes various information regarding the change made by the user in the data and that footer is appended to the original image data using compression algorithm. Similarly, the user can fetch the modified image data using decompression algorithm along with the original image data. This mechanism can be used with various base file formats such as JPG, PNG, GIF, etc. and also ensures the security of the data.

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“…The modified data model known from the FELICS algorithm invented by Howard and Vitter [19] is used. For prediction errors of pixels in the first column of an image a prediction error of the above pixel is used as a context, for prediction errors of the remaining pixels the preceding residuum symbol, i.e., a prediction error of pixel's lefthand neighbor, is used as a context.…”

Section: The Data Modelmentioning

confidence: 99%

“…For encoding residuum symbols we use a family of prefix codes based on the Golomb-Rice family. For fast and adaptive modeling we use a simple context data model based on a model of the FELICS algorithm [19] and the method of reduced model update frequency [20]. The algorithm was designed to be simple and fast.…”

Section: Overviewmentioning

confidence: 99%

“…The algorithm, designed primarily to achieve the high compression speed, is based on the linear prediction, modified Golomb-Rice code family and a very fast prediction error modeling method. The operation of updating the data model, which is based on the data model known from the FELICS algorithm [19], although fast as compared to many other modeling methods would be the most complex element of the algorithm. Therefore we apply the reduced model update frequency method that increases the overall compression speed by a couple of hundred percent at the cost of worsening the compression ratio by a fraction of a percent.…”

Section: Introductionmentioning

confidence: 99%

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Simple fast and adaptive lossless image compression algorithm

Starosolski

2006

Softw Pract Exp

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In this paper we present a new lossless image compression algorithm. To achieve the high compression speed we use a linear prediction, modified Golomb-Rice code family, and a very fast prediction error modeling method. We compare the algorithm experimentally with others for medical and natural continuous tone grayscale images of depths of up to 16 bits. Its results are especially good for big images, for natural images of high bit depths, and for noisy images. The average compression speed on Intel Xeon 3.06 GHz CPU is 47 MB/s. For big images the speed is over 60 MB/s, i.e., the algorithm needs less than 50 CPU cycles per byte of image.

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Fast and efficient lossless image compression

Cited by 151 publications

References 10 publications

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

Embedded and Flexible Image File Version Control System for Managing Multiple Minor Changes

Simple fast and adaptive lossless image compression algorithm

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