Implementation of image compression on hardware platform
The image compression in which I use is JPEG. JPEG has been around for many decades and it has also been a standardized means of image transmission. For JPEG, there are lossless and lossy compression. Lossy compression will be implemented for this project. Lossy compression will result in a lower qu...
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sg-ntu-dr.10356-745532023-07-07T15:54:34Z Implementation of image compression on hardware platform Koh, Aloysius Jia Lun Ho Duan Juat School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision The image compression in which I use is JPEG. JPEG has been around for many decades and it has also been a standardized means of image transmission. For JPEG, there are lossless and lossy compression. Lossy compression will be implemented for this project. Lossy compression will result in a lower quality image as compared to the original, but it requires fewer resources and lower latency. The values and test image are according to industry’s standard and will be implemented as a means of testing the algorithm effectiveness on Zedboard and on a hardware platform. Given that the coding and methods used were simple and not effective, it would only be a rough comparison. “Lena”, a standardized test image was converted to RGB format and adjusted to 256 x 256 resolution, prior to the compression, to accommodate for the low storage space. The image will be converted to YCrCb color space for the JPEG compression and back to RGB, to be displayed through a 12-bit VGA cable to a monitor. The quantization stage of the JPEG compression algorithm determines the quality of the decompressed output image. The higher the quality factor, the lower the compression but higher the output quality. Multiple quality factors in a factor of 10 from 0 to 100 are being tested. However, there are some setbacks. The internal memory of the BRAM is not large enough to support a Q-factor of above 80. As a result, only Q-factor from 0-80 are manipulated. Bachelor of Engineering 2018-05-21T07:19:06Z 2018-05-21T07:19:06Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74553 en Nanyang Technological University 82 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Koh, Aloysius Jia Lun Implementation of image compression on hardware platform |
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The image compression in which I use is JPEG. JPEG has been around for many decades and it has also been a standardized means of image transmission. For JPEG, there are lossless and lossy compression. Lossy compression will be implemented for this project. Lossy compression will result in a lower quality image as compared to the original, but it requires fewer resources and lower latency.
The values and test image are according to industry’s standard and will be implemented as a means of testing the algorithm effectiveness on Zedboard and on a hardware platform. Given that the coding and methods used were simple and not effective, it would only be a rough comparison.
“Lena”, a standardized test image was converted to RGB format and adjusted to 256 x 256 resolution, prior to the compression, to accommodate for the low storage space. The image will be converted to YCrCb color space for the JPEG compression and back to RGB, to be displayed through a 12-bit VGA cable to a monitor.
The quantization stage of the JPEG compression algorithm determines the quality of the decompressed output image. The higher the quality factor, the lower the compression but higher the output quality. Multiple quality factors in a factor of 10 from 0 to 100 are being tested. However, there are some setbacks. The internal memory of the BRAM is not large enough to support a Q-factor of above 80. As a result, only Q-factor from 0-80 are manipulated. |
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Ho Duan Juat |
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Ho Duan Juat Koh, Aloysius Jia Lun |
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Final Year Project |
author |
Koh, Aloysius Jia Lun |
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Koh, Aloysius Jia Lun |
title |
Implementation of image compression on hardware platform |
title_short |
Implementation of image compression on hardware platform |
title_full |
Implementation of image compression on hardware platform |
title_fullStr |
Implementation of image compression on hardware platform |
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Implementation of image compression on hardware platform |
title_sort |
implementation of image compression on hardware platform |
publishDate |
2018 |
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http://hdl.handle.net/10356/74553 |
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1772825954206351360 |