APPLIED ASPECTS OF COMPUTER SCIENCE
P. B. Bogdanov, O. J. Sudareva JPEG image decoding on the KOMDIV microprocessors
CONTROL SYSTEMS
SOFTWARE ENGINEERING
BIOINFORMATICS AND MEDICINE
DATA PROCESSING AND ANALYSIS
SECURITY ISSUES
P. B. Bogdanov, O. J. Sudareva JPEG image decoding on the KOMDIV microprocessors

Abstract.

In this paper we consider possible application of special-purpose massively-parallel SIMD-coprocessor CP2 for digital image compression. The CP2 coprocessor is designed by and used in several products of ISR RAS, and it is capable of computations with real and complex numbers. We take the JPEG digital image compression standard as an example and analyse the JPEG decompression algorithm for further implementation on CP2.

Keywords:

KOMDIV, CP2, JPEG, JFIF, DCT, libjpeg-turbo.

PP. 3-16.

DOI 10.14357/20718632190101

References

1. Sudareva, O.J. 2014. Effektivnaya realizatsiya algoritmov bystrogo preobrazovaniya Furje I svertki na microprocessore KOMDIV128-RIO [The effective implementation of the Fast Fourier Transform and convolution algorithms for the KOMDIV128-RIO microprocessor]. Moscow: ISR RAS. 266 p.
2. Rajko, G.O., J.A. Pavlovskij, and V.S. Melkanovich. 2014. Techologiya programmirovaniya mnogoprocessornoy obrabotki signalov na vychislitelnyh ustroistvah semeistva “KOMDIV” [Technology of programming of multiprocessor processing of hydroacoustic signals on “KOMDIV” computer set]. Gidroakustika [Hydroacoustics]. Issue 20(2). SPb.: The “Oceanpribor” concern. 118 p.
3. Bogdanov, P.B., and O.J. Sudareva. 2016. Primenenie otechestvennyh specializirovannyh processorov semeystva KOMDIV v nauchnyh raschetah [The applicability of Russian special-purpose KOMDIV microprocessor series for scientific computations]. Informatsionnye technologii i vychislitelnye sistemy [Information Technologies and Computational Systems]. 3:45–65.
4. Bogdanov, P.B., and O.J. Sudareva. 2017. Proizvoditelnost processorov KOMDIV na ryade tipovyh raschetnyh zadach [The KOMDIV microprocessors performance on a number of typical computational problems]. Informatsionnye technologii i vychislitelnye sistemy [Information Technologies and Computational Systems]. 4:104–111.
5. International Standard ISO/IEC 10918-1:1993(E). CCITT Rec. T.81 (1992 E). Information technology — Digital compression and coding of continuous-tone still images — Requirements and Guidelines.
6. International Standard ISO/IEC 10918-5:2012(E). Rec. ITU-T T.871 (05/2011). Information technology — Digital compression and coding of continuous-tone still images — JPEG File Interchange Format (JFIF).
7. libjpeg-turbo — homepage. Available at: https://libjpegturbo.org/ (accessed December 23, 2018).
8. Zubkovsky, P.S. 2013. Opisanie vektornogo soprocessora processora K64-M, versiya 2.7 [The specification of vector coprocessor of the K64-M procession, version 2.7]. M.: ISR RAS.
9. Wallace, G.K. The JPEG Still Image Compression Standard. 1992. IEEE Transactions on Consumer Electronics. 38(1):18–34. doi: 10.1109/30.125072.
10. Milleson, J. 2014. Partial Image Decoding On The GPU For Mobile Web Browsers. Master’s Thesis. Chalmers University of Technology, University of Gothenburg. 53 p.
11. Huffman, D.A. 1952. A method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE. 40(9):1098–1101. doi: 10.1109/JRPROC.1952.273898.
12. Ahmed, N., T. Natarajan, and K.R. Rao. 1974. Discrete Cosine Transform. IEEE Transactions on Computers. C-23(1):90–93. doi: 10.1109/T-C.1974.223784.
13. Kerr, D.A. 2012. Chrominance Subsampling in Digital Images. Issue 3. Available at: http://dougkerr.net/Pumpkin/articles/Subsampling.pdf (accessed December 23, 2018).
14. Klein, S.T., and Y. Wiseman. 2003. Parallel Huffman Decoding with Applications to JPEG Files. The Computer Journal. 46(5):487–497.
15. Plumadore, K. 2018. GPU Parallel Huffman Decoding. U.S. Patent No. 9906239. Available at: http://www.freepatentsonline.com/9906239.html (accessed December 23, 2018).
16. Singh, S.P., A. Bhasin, and K. Saha. 2011. Parallelization of Variable Length Decoding. U.S. Patent No. 2011/0150351. Available at: https://patents.google.com/patent/US20110150351 (accessed December 23, 2018).
17. Chieppe, P. 2017. JPEG decoding using «end of block» markers to concurrently partition channels on a GPU. Australian National University, COMP4560. Available at: http://courses.cecs.anu.edu.au/courses/CSPROJECTS/17S1/Re-ports/Patrick_Chieppe_Report.pdf (accessed December 23, 2018).
18. Makhoul, J. 1980. A Fast Cosine Transform in One and Two Dimensions. IEEE Transactions on Acoustics, Speech and Signal Processing. 28(1):27–34. doi: 10.1109/12.895848.
19. Chen, W.-H., C. Smith, and S. Fralick. 1977. A Fast Computational Algorithm for the Discrete Cosine Transform. IEEE Transactions on Communications. 25(9):1004–1009. doi: 10.1109/TC.1982.1676108.
20. Loeffler, C., A. Ligtenberg, and G.S. Moschytz. 1989. Practical Fast 1-D DCT Algorithms with 11 Multiplications. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 2:988–991. doi: 10.1109/ICASSP.1989.266596.
21. Duhamel, P., and H. H'Mida. 1987, New 2n DCT Algorithms Suitable for VLSI Implementation. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. 12:1805–1809. doi: 10.1109/ICASSP.1987.1169491.
22. Arai, Y., T. Agui, and M. Nakajima. 1987. A fast DCT-SQ scheme for images. Transactions of the IEICE. E-71(11):1095‒1097.
23. Popović, M., and T. Stojić. 1998. The Fast Computation of DCT in JPEG Algorithm. 9th European Signal Processing Conference (EUSIPCO 1998). 1‒4.
 

2024 / 01
2023 / 04
2023 / 03
2023 / 02

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".