New adaptive color quantization method based on self-organizing maps
Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensit...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2009
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| Online Access: | https://hdl.handle.net/10356/90593 http://hdl.handle.net/10220/6012 |
| _version_ | 1811686644231700480 |
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| author | Chang, Chip Hong Xu, Pengfei Xiao, Rui Srikanthan, Thambipillai |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chang, Chip Hong Xu, Pengfei Xiao, Rui Srikanthan, Thambipillai |
| author_sort | Chang, Chip Hong |
| collection | NTU |
| description | Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage overhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme. |
| first_indexed | 2024-10-01T05:03:41Z |
| format | Journal Article |
| id | ntu-10356/90593 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:03:41Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/905932020-03-07T14:02:37Z New adaptive color quantization method based on self-organizing maps Chang, Chip Hong Xu, Pengfei Xiao, Rui Srikanthan, Thambipillai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage overhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme. Published version 2009-08-03T05:47:30Z 2019-12-06T17:50:30Z 2009-08-03T05:47:30Z 2019-12-06T17:50:30Z 2005 2005 Journal Article Chang, C. H., Xu, P., Xiao, R. & Srikanthan, T. (2005). New adaptive color quantization method based on self-organizing maps. IEEE Transactions on Neural Networks, 16(1), 237-249. 1045-9227 https://hdl.handle.net/10356/90593 http://hdl.handle.net/10220/6012 10.1109/TNN.2004.836543 en IEEE transactions on neural networks IEEE Transactions on Neural Networks © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site. 13 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chang, Chip Hong Xu, Pengfei Xiao, Rui Srikanthan, Thambipillai New adaptive color quantization method based on self-organizing maps |
| title | New adaptive color quantization method based on self-organizing maps |
| title_full | New adaptive color quantization method based on self-organizing maps |
| title_fullStr | New adaptive color quantization method based on self-organizing maps |
| title_full_unstemmed | New adaptive color quantization method based on self-organizing maps |
| title_short | New adaptive color quantization method based on self-organizing maps |
| title_sort | new adaptive color quantization method based on self organizing maps |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/90593 http://hdl.handle.net/10220/6012 |
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