Chimera: A New Efficient Transform for High Quality Lossy Image Compression
A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/3/378 |
| _version_ | 1817992998176161792 |
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| author | Walaa Khalaf Ahmad Saeed Mohammad Dhafer Zaghar |
| author_facet | Walaa Khalaf Ahmad Saeed Mohammad Dhafer Zaghar |
| author_sort | Walaa Khalaf |
| collection | DOAJ |
| description | A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2.0272</mn> </mrow> </semantics> </math> </inline-formula> for DCT, <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1.179</mn> </mrow> </semantics> </math> </inline-formula> for WT and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4.301</mn> </mrow> </semantics> </math> </inline-formula> for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.1108</mn> </mrow> </semantics> </math> </inline-formula> for DCT, <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.051</mn> </mrow> </semantics> </math> </inline-formula> for WT and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.175</mn> </mrow> </semantics> </math> </inline-formula> for KLT. |
| first_indexed | 2024-04-14T01:33:54Z |
| format | Article |
| id | doaj.art-5e7b4e12093f4466928b8d9f1955717c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-14T01:33:54Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-5e7b4e12093f4466928b8d9f1955717c2022-12-22T02:20:04ZengMDPI AGSymmetry2073-89942020-03-0112337810.3390/sym12030378sym12030378Chimera: A New Efficient Transform for High Quality Lossy Image CompressionWalaa Khalaf0Ahmad Saeed Mohammad1Dhafer Zaghar2Computer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, IraqComputer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, IraqComputer Engineering Department, College of Engineering-Mustansiriyah University, Baghdad 10047, IraqA novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2.0272</mn> </mrow> </semantics> </math> </inline-formula> for DCT, <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1.179</mn> </mrow> </semantics> </math> </inline-formula> for WT and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4.301</mn> </mrow> </semantics> </math> </inline-formula> for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.1108</mn> </mrow> </semantics> </math> </inline-formula> for DCT, <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.051</mn> </mrow> </semantics> </math> </inline-formula> for WT and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0.175</mn> </mrow> </semantics> </math> </inline-formula> for KLT.https://www.mdpi.com/2073-8994/12/3/378image processingimage compressiondiscrete cosine transformwavelet transformmachine learning |
| spellingShingle | Walaa Khalaf Ahmad Saeed Mohammad Dhafer Zaghar Chimera: A New Efficient Transform for High Quality Lossy Image Compression Symmetry image processing image compression discrete cosine transform wavelet transform machine learning |
| title | Chimera: A New Efficient Transform for High Quality Lossy Image Compression |
| title_full | Chimera: A New Efficient Transform for High Quality Lossy Image Compression |
| title_fullStr | Chimera: A New Efficient Transform for High Quality Lossy Image Compression |
| title_full_unstemmed | Chimera: A New Efficient Transform for High Quality Lossy Image Compression |
| title_short | Chimera: A New Efficient Transform for High Quality Lossy Image Compression |
| title_sort | chimera a new efficient transform for high quality lossy image compression |
| topic | image processing image compression discrete cosine transform wavelet transform machine learning |
| url | https://www.mdpi.com/2073-8994/12/3/378 |
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