Histogram modification based lossy image compression scheme using Huffman coding

Rahman, Md. Atiqur; Rabbi, M.M. Fazle; Rahman, Md. Mijanur; Islam, Md. Masudul; Islam, Md. Rashedul

doi:10.1109/ceeict.2018.8628092

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…In lossy compression, irrelevant and less significant data are removed permanently, whereas, in lossless compression, every detail is preserved and only statistical redundancy is eliminated. In short, lossy compression allows for slight degradation in the data, while lossless methods perfectly reconstruct the data from its compressed form [5][6][7][8]. There are many applications for lossless data compression techniques, such as medical imagery, digital radiography, scientific imaging, zip file compression, museums/art galleries, facsimile transmissions of bitonal images, business documents, machine vision, the storage and sending of thermal images taken by nano-satellites, observation of forest fires, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The probability of the kth values is calculated while using Equation (5), where N is the total number of values in an image. If L k represents the length of the code-word for the values S k , then the length of the average code-word can be calculated using Equation (6), where SL is the total number of different values. Table 1 shows the variable length coding for the image shown in Figure 6.…”

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confidence: 99%

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The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

2021

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A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

2021

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“…MD Atiqur Rahman et al proposed a lossy compression method combining histogram modification and Huffman coding, which reduces the number of bits used in Huffman coding by changing the pixel value. The results show that the average code length of this method is about 30% shorter than the original Huffman coding [9].…”

Section: Introductionmentioning

confidence: 95%

Image compression and reconstruction based on GUI and Huffman coding

Zhan

2023

J. Phys.: Conf. Ser.

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Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

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“…The Run-Length Encoding and arithmetic coding methods are used to encode the resultant transforms. Rahman et al [15] proposed a histogram modification-based lossy compression method using Huffman coding. The method reduces the total number of different probabilities and pixels value by increasing the frequencies in the Huffman coding.…”

Section: Literature Reviewmentioning

confidence: 99%

Lossy codecs for digital image signatures

Kommey

Kotey

Adom-Bamfi

et al. 2021

Sustainable Engineering and Innovation

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Most applications in recent times make use of images one way or the other. As physical devices for capturing images improve, the quality and sizes of images also increase. This causes a significant footprint of images on storage devices. There is ongoing research to reduce the footprint of images on storage. Since storage is a finite resource, the goal is to reduce the sizes of images while maintaining enough quality pleasant to the human eye. In this paper, the design of two lossy codecs for compressing grayscale digital signature images has been presented. The algorithms used either simple thresholding or transform coding to introduce controlled losses into the image coding chain. This was to reduce, to a great extent, the average number of bits per pixel required to represent the images. The codecs were implemented in MATLAB and experiments were conducted with test images to study the performances of the algorithms.

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Histogram modification based lossy image compression scheme using Huffman coding

Cited by 19 publications

References 15 publications

The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

The Impact of State-of-the-Art Techniques for Lossless Still Image Compression

Image compression and reconstruction based on GUI and Huffman coding

Lossy codecs for digital image signatures

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