United coding method for compound image compression
This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video...
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| Vydané v: | Multimedia tools and applications Ročník 71; číslo 3; s. 1263 - 1282 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Boston
Springer US
01.08.2014
Springer Springer Nature B.V |
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| ISSN: | 1380-7501, 1573-7721 |
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| Abstract | This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 16 × 16 pixels to 64 × 64 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2–5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264. |
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| AbstractList | This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 16 × 16 pixels to 64 × 64 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2–5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264. This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 16×16 pixels to 64×64 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2-5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264.[PUBLICATION ABSTRACT] This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 1616 pixels to 6464 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2-5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264. |
| Author | Wang, Shuhui Lin, Tao |
| Author_xml | – sequence: 1 givenname: Shuhui surname: Wang fullname: Wang, Shuhui email: wangshuhui_cn@yahoo.com.cn organization: VLSI Lab, Tongji University – sequence: 2 givenname: Tao surname: Lin fullname: Lin, Tao organization: VLSI Lab, Tongji University |
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| Cites_doi | 10.1117/1.482609 10.1109/TIP.2005.849776 10.1109/IWSSIP.2007.4381171 10.1109/ICIP.1999.817222 10.1109/ICME.2009.5202873 10.1117/12.532433 10.1109/ICIP.1999.821603 10.1049/ip-cds:19990535 10.1109/CISP.2010.5647270 10.1109/83.847840 10.1109/ICIP.2005.1529812 10.1109/ICIP.1999.821601 10.1109/TIP.2010.2049181 10.1109/ICIP.2002.1038906 10.1109/LSP.2009.2014285 10.1109/TIP.2009.2038636 10.1109/TIT.1977.1055714 10.1109/ICIP.2006.312816 10.1109/TIP.2007.899036 10.1109/83.988964 10.1109/34.777372 |
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| Keywords | Dictionary-entropy coding Compound image and video United coding Hybrid coding Lossless coding Video coding Cloud computing Dictionaries Image processing Data compression Video signal Lossless compression Distributed computing Optimization Image coding Pervasive computing Image compression Text PNG image RLE encoding Graphics Experimental result Compression ratio Outsourcing Signal to noise ratio |
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| References_xml | – reference: FranciscoNCRodriguesNMMda SilvaEABde CarvalhoMBde FariaSMMSilvaVMMScanned compound document encoding using multiscale recurrent patternsIEEE Trans Image Process201019102712272410.1109/TIP.2010.20491812815067 – reference: ZivJLempelAA universal algorithm for sequential data compressionIEEE Trans Inf Theory197723333734310.1109/TIT.1977.10557140379.94010530215 – reference: AlzinaMSzpankowskiWGramaA2D-pattern matching image and video compression: theory, algorithms, and experimentsIEEE Trans Image Process200211331833110.1109/83.9889641889009 – reference: Huttenlocher D, Felzenszwalb P, Rucklidge W (1999) Digipaper: a versatile color document image representation. In: Proc. IEEE Int. Conf. Image Processing, vol.1. 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| SubjectTerms | Algorithms Analysis Applied sciences Artificial intelligence Cloud computing Coders Coding Coding standards Coding, codes Computer Communication Networks Computer graphics Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Data compression Data Structures and Information Theory Dictionaries Encoding Exact sciences and technology Image coding Image processing systems Information, signal and communications theory Lossless Multimedia computer applications Multimedia Information Systems Optimization Pattern recognition. Digital image processing. Computational geometry Pictures Pixels Protocol Rendering Signal and communications theory Software Special Purpose and Application-Based Systems Studies Telecommunications and information theory Video compression |
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