Effective lossless compression for medical image sequences using composite algorithm

Ukrit, M. Ferni; Suresh, G.

doi:10.1109/iccpct.2013.6528908

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Generally, there are two categories of image compression, lossy and lossless [10]. In lossless compression, the recovered data identical to the original, whereas the recovered data is a close replica of the original with minimal data loss in the case of lossy compression [11,12]. The study shows that both compressions had significant advantages and disadvantages in the field of data compression.…”

Section: Medical Image Compressionmentioning

confidence: 99%

“…Another part of the image's content is known as the background and the image's most ignored part. In the medical field, Many different parameters can be used to describe and compare the performance of a compression technique such as mean square error (MSE), peak signal to noise ratio (PSNR), compression ratio (CR), and time consumption to compress the image [11,17,18]. The compression ratio indicates the efficiency of the compression algorithm.…”

Section: Medical Image Compressionmentioning

confidence: 99%

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A review on region of interest-based hybrid medical image compression algorithms

Aziz

Sam

Mohamed³

et al. 2020

TELKOMNIKA

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Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms.

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Section: Medical Image Compressionmentioning

confidence: 99%

Section: Medical Image Compressionmentioning

confidence: 99%

A review on region of interest-based hybrid medical image compression algorithms

Aziz

Sam

Mohamed³

et al. 2020

TELKOMNIKA

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show abstract

“…However, it showed no superior performance than HBMO algorithm. In the year 2013, [23] integrated Super-Spatial Structure Prediction with inter-frame coding to attain better CR. At first, Super-Spatial Structure Prediction algorithm is employed with a fast block-matching process (Diamond Search method).…”

Section: Related Studiesmentioning

confidence: 99%

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

Preethi,

Loganathan

2019

IJEAT

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In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

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“…But lossless scheme is reversible and this represents an image signed with the smallest possible number of bits without loss of any information, and the compression ratio achieved is low [12], such as LOCO-I [13], CALIC [14], JPEG-LS [15] and JPEG2000 (5/3) [19]. Applications like satellite image compression, medical image compression where any loss in data may lead to incorrect prediction or diagnosis, lossless compression methods are used.…”

Section: Introductionmentioning

confidence: 98%

An improved medical image compression technique with lossless region of interest

Zuo

Lan

Deng

et al. 2015

Optik

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Effective lossless compression for medical image sequences using composite algorithm

Cited by 10 publications

References 7 publications

A review on region of interest-based hybrid medical image compression algorithms

A review on region of interest-based hybrid medical image compression algorithms

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

An improved medical image compression technique with lossless region of interest

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