research paper in image compression

Analysis of Different Image Compression Techniques: A Review

Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2022

4 Pages Posted: 16 Feb 2022

Garima Garg

IKGPTU Mohali Campus, Mohali, India

Raman Kumar

Date Written: February 10, 2022

The availability of images in a wide variety of applications has expanded due to technological developments that have not to influence the variety of image operations, the availability of advanced image modification software, or image management. Despite technological breakthroughs in storage and transmission, demand for storage capacity and communication bandwidth exceeds available capacity. As a result, image compression has proven to be a helpful technique. When it comes to image compression, we don't just focus on lowering size; we also focus without sacrificing image quality or information. The survey outlines the primary image compression algorithms, both lossy and lossless, and their benefits, drawbacks, and research opportunities. This examination of several compression techniques aids in the identification of advantageous qualities and the selection of the proper compression method. We suggested some general criteria for choosing the optimum compression algorithm for an image based on the review.

Keywords: Image Compression, types of images, performance assessment metrics, compression techniques

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Comprehensive Review on Lossy and Lossless Compression Techniques

• Review Paper
• Published: 28 October 2021
• Volume 103 , pages 1003–1012, ( 2022 )

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• S. Elakkiya   ORCID: orcid.org/0000-0001-7432-409X 1 &
• K. S. Thivya 1  

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Images are now employed as data in a variety of applications, including medical imaging, remote sensing, pattern recognition, and video processing. Image compression is the process of minimizing the size of images by removing or grouping certain parts of an image file without affecting the quality, thereby saving storage space and bandwidth. Image compression plays a vital role where there is a need for images to be stored, transmitted, or viewed quickly and efficiently. There are different techniques through which images can be compressed. This paper mainly focuses on the survey of basic compression techniques available and the performance metrics that are used to evaluate them. In addition to this, it also provides a review of important pieces of the literature relating to advancements in the fundamental lossy and lossless compression algorithms.

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Elakkiya, S., Thivya, K.S. Comprehensive Review on Lossy and Lossless Compression Techniques. J. Inst. Eng. India Ser. B 103 , 1003–1012 (2022). https://doi.org/10.1007/s40031-021-00686-3

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Received : 07 January 2021

Accepted : 03 October 2021

Published : 28 October 2021

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DOI : https://doi.org/10.1007/s40031-021-00686-3

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Lossless image compression techniques: a state-of-the-art survey.

1. Introduction

2. the state-of-the-art techniques, 2.1. run-length coding, 2.1.1. run-length encoding procedure.

• Calculate the difference (B = [1 −1 0 0 1 0 0 0 0 0 0 0 0 −2 −1 0 0 0 3 0 0 0 0 0 0 0 0 −2 0 0 2 0 −4 0 0 −1 0 0 3 0 0 0 0 0 0 0 0 −4 0 1]) using f ( x ) = f ( x + 1 ) − f ( x ) .
• Assign 1 to each non-zero data of B and we get B = [1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1].
• Save the positions of all ones into an array (position = [1 2 5 14 15 19 28 31 33 36 39 48 50]) and the corresponding data into (items = [6 7 6 7 5 4 7 5 7 3 2 5 1]) from A. The array position and items are stored or sent as the encoded list of the original 50 elements.

2.1.2. Run-Length Decoding Procedure

• Read each element from the array items and write the element repeatedly until its corresponding number in the position array is found.

2.1.3. Analysis of Run-Length Coding Procedure

2.2. shannon–fano coding, 2.2.1. shannon–fano encoding style.

• Find the distinct symbols (N) and their corresponding probabilities.
• Sort the probabilities in descending order.
• Divide them into two groups so that the entire sum of each group is as equal as possible, and make a tree.
• Assign 0 and 1 to the left and right group, respectively.
• Repeat steps 3 and 4 until each element becomes a leaf node on a tree.

2.2.2. Shannon–Fano Decoding Style

• Read each bit from an encoded bitstream and scan the tree until a leaf node is found. At the point when a leaf hub is discovered, read the symbol of the node as decoded value, and the process will proceed until scanning of the encoded bitstream is finished.

2.2.3. Analysis of Shannon–Fano Coding

2.3. huffman coding, 2.3.1. huffman encoding style.

• List the probabilities of a gray-scale image in descending order.
• Form a new node of a tree with the sum of the two lowest probabilities on the list and rearrange them in the same order for the proceeding process. This process will continue until the end.
• Assign 0 and 1 to each left and right branch of the tree, respectively.

2.3.2. Huffman Decoding Style

• Recreates the equivalent Huffman tree built in the encoding step using the probabilities.
• Each bit is scanned from the encoded bitstream and traverses the tree node by node until a leaf node is reached. At the point when a leaf node is discovered, the symbol is predicted from the node. This process will proceed until finished.

2.3.3. Analysis of Huffman Coding

2.4. lempel–ziv–welch (lzw) coding, 2.4.1. lzw encoding procedure.

• Assign 0–255 in a table and set the first data from the input file to FD,
• Repeat steps 3 to 4 until reading is finished,
• ND = Read the next data,
• FD = FD + ND,
• Store the code for FD as encoded data and insert FD + ND to the table. In addition, set FD = ND.

2.4.2. LZW Decoding Procedure

• Assign 0–255 in a table and scan the first encoded value and assign it to FEV. Later, send the translation of FEV to the output.
• Repeat steps 3 to 4 until the reading of the encoded file ends.
• NC = read next code from encoded file.
• Assign the translation of FEV to DS and perform DS = DS + NC
• Assign the translation of NC to DS, the first code of DS to NC, NC to FEV and add FEV+NC into the table. Furthermore, send DS to the output.

2.4.3. Analysis of LZW Coding

2.5. arithmetic coding, 2.5.1. arithmetic encoding procedure.

• limit = UL − LL,
• UL = LL + limit * CF[N − 1],
• LL = LL + limit * CF[N].

2.5.2. Arithmetic Decoding Procedure

• if(CF[N] < = (tag − LL)/(UL − LL) < CF[N − 1]), (a) limit = UL − LL, (b) UL = LL + limit*CF[N − 1], (c) LL = LL + limit*CF[N], (d) return N.
• tag = 0.955195. Since 0.9 < = tag < = 1.0, Thus, decoded value is 2 because the symbol 2 is in range.
• NT1 = (tag − LL)/r = 0.55195 and it is in between 0.5 and 0.7, so the decoded value is 3.
• NT2 = (NT1 − LL)/r = 0.25975 and it is in between 0 and 0.5, so the decoded value is 4.
• NT3 = (NT2 − LL)/r = 0.5195 and it is in between 0.5 and 0.7, so the decoded value is 3.
• NT4 = (NT3 − LL)/r = 0.0975 and it is in between 0 and 0.5, so the decoded value is 4.
• NT5 = (NT4 − LL)/r = 0.195 and it is in between 0 and 0.5, so the decoded value is 4.
• NT6 = (NT5 − LL)/r = 0.39 and it is in between 0 and 0.5, so the decoded value is 4.
• NT7 = (NT6 − LL)/r = 0.78 and it is in between 0.7 and 0.9, so the decoded value is 1.
• NT8 = (NT7 − LL)/r = 0.4 and it is in between 0 and 0.5, so the decoded value is 4.
• NT9 = (NT8 − LL)/r = 0.8 and it is in between 0.7 and 0.9, so the decoded value is 1.

2.5.3. Analysis of Arithmetic Coding Procedure

3. experimental results and analysis, 4. conclusions, author contributions, conflicts of interest.

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Rahman, M.A.; Hamada, M. Lossless Image Compression Techniques: A State-of-the-Art Survey. Symmetry 2019 , 11 , 1274. https://doi.org/10.3390/sym11101274

Rahman MA, Hamada M. Lossless Image Compression Techniques: A State-of-the-Art Survey. Symmetry . 2019; 11(10):1274. https://doi.org/10.3390/sym11101274

Rahman, Md. Atiqur, and Mohamed Hamada. 2019. "Lossless Image Compression Techniques: A State-of-the-Art Survey" Symmetry 11, no. 10: 1274. https://doi.org/10.3390/sym11101274

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