• 沒有找到結果。

類神經網路在影像壓縮應用上之研究A Study on the Neural Networks for Image Compression

N/A
N/A
Protected

Academic year: 2021

Share "類神經網路在影像壓縮應用上之研究A Study on the Neural Networks for Image Compression"

Copied!
4
0
0

加載中.... (立即查看全文)

全文

(1)

 

B!tuvez!po!uif!ofvsbm!ofuxpslt!gps!jnbhf!dpnqsfttjpo!

!

   

        

  !"#$%&'($) *+

,-./

/0012300451617408749:

;-< ==> 

     !" # $%&'()*+,-./01 2345 6 789:;<=> ?@A#; BCD EFGAHIJ&'(K LM2NO#P@Q+RS HIJ 34 TUVWXY Z[\Y]8 Z[VWXY  \Y]8 2^_`abcdZ[R efghijkklmfZ[nXVopqrZ[ "#stuv!2HIJ34wRxuS 2yz{|2}~€)‚HIJ ƒ„R… 2†‡ˆ‰‚Š"#; ‹Œ_Ž(,$HIJ&' (2yz‘EFTU’“-”2 \o>•–—˜™š HIJZ[`“-‘›œ XVopq mf Z[#EF2AHIJ2}~€ž ŸAHIJ ¡ƒ„`AHIJ ¢ £¤_¥#; ;    \˜; ¦—§; —˜; ¨©™; ¨§—šª¨ª«¬—­; ˜¨—¨ª®; ®«¯°§™˜˜ª«¬; —­±«§ª¨©¯; ª˜; ®«¬®™§¬™š²; ª¯—±™; ®«¯°§™˜˜ª«¬; ª˜; —; ³™§´; ¯—¨µ§™; ¨™®©¬ª¶µ™·; ; >©™§™; ™¸ª˜¨; ¹™­­•º¬«¹¬; —­±«§ª¨©¯˜²; —¯«¬±;¨©™¯²;789:;<=>;–™ª¬±;¨©™;¯«˜¨;¦§™¶µ™¬¨­´;µ˜™š; ¯™¨©«š·; V«;¦—§²;¯—¬´;š—¨—;®«¯°§™˜˜ª«¬;—­±«§ª¨©¯˜;–—˜™š;«¬; ¬™µ§—­;¬™¨¹«§º˜;©—³™;–™™¬;°§«°«˜™š²;™·±·²;VWXY;¯«š™­²; \Y]8; ¯«š™­²; VWXY; —¬š; \Y]8; ©´–§ªš; ¯«š™­˜; —¬š; XVopq; ¯«š™­·; ; »«¹™³™§²; ¨©™; ™¸ª˜¨ª¬±; ¬™µ§—­; ª¯—±™; ®«¯°§™˜˜ª«¬˜;˜¨ª­­;©—³™;¯—¬´;š§—¹–—®º˜·; ¼¬; ¨©ª˜; °§«½™®¨²; —; ¬™¹; \o>•–—˜™š; ª¯—±™; ®«¯°§™˜˜ª«¬; —¬š; —¬; ª¯°§«³™š; XVopq; ¯«š™­; —§™; °§«°«˜™š·; ; ¾˜ª¬±; ¨©™˜™; ¯™¨©«š˜²;¨©™; ¨§—ª¬ª¬±; ¨ª¯™; ®—¬; –™; š™®§™—˜™š·; ; ¼¬; —ššª¨ª«¬²; ¨©™´; ¯—º™; ¨©™;®«¬˜¨§µ®¨ª«¬; «¦;¬™µ§—­;¬™¨¹«§º˜;™—˜ª™§²;—¬š;¨©™;µ˜™;«¦;¬™µ§—­;¬™¨¹«§º˜; «¬;ª¯—±™;®«¯°§™˜˜ª«¬;¯«§™;°§—®¨ª®—­·; ;  HIJ¿,ÀÁÂÃÄÅ2‹Œ Æ “ÇÈ%2&'(RxÉÊ2ËÌ#ÇÍ% &'(£RÎb2Ï¥ÐnÑrÒÓÔÕÖ×nØr ÒÖ^_ÏÙ×nÚr£ÛÜÝÞ"#ßP@Q t- ¢+R 2HIJƒ„ÛÜ(à TU¿+ Páœáâ2 ãäå u æv’./çè2éê#; HIJ£Rëìíî2ÝÞ#ëìíîï ,ð“Êñò óUô6õötuò2÷R Ïøù6EFúˆ !t“Êñ2ò)û ‰üÎÇF2Ïøý¤#5þEF¤6vëìí îÈò,RÌ2éê¿ï,HIJ B£Rò2ÝÞù6 F AHIJ34K t-NO#tÁÂƀ +Rú2HIJ    & ' ( T U     —µ¨«•—˜˜«®ª—¨ª³™; ¯µ­¨ª•­—´™§;°™§®™°¨§«¬;\Y]8;ÑÚ˜™­¦•«§—¬±ªª¬±; ¦™—¨µ§™;¯—°;VWXY;Ñ\Y]8  VWXY ^_c dZ[Ñ\Y]8  VWXY abcdZ[ÑR efghijkklmfZ[XVopq;Ø"#; 6¢ùT2-+v!2HIJ3 4 6RefghijkklmfZ[2é Ý?Ç2éÝÝ{ 789:\Y]8VWXY cdZ[2éÝÍÆ\Y]8 Z[ Æ ) VWXY 2éÝ,?Ë#stwRÎ2yz ˆ‰6 Ð; !";}~{#./$–—®º•°§«°—±—¨ª«¬ Û Ü(à` ˜™­¦•«§±—¬ª—¨ª«¬ ÛÜ(à%ˆ‰&' ()}~ßP*P‰+@Q#; ,";-ˆ./ŠHIJƒ„012 -‡012IJ3-‡4«©«¬™¬ 2IJ3 -‡"#; 5";… 2†‡-ˆ./ŠÛÜ67 ˜™­¦•«§±—¬ª—¨ª«¬ ÛÜ(à 2 ¬™ª±©–«§ Ê8` ¬™ª±©–«§ Ê89267":#; ;";éêwˆ<Ñ#; R=>¢?HIJ&'(2yz t-‹ŒCD EF$tuyz$@ÎABx /CžŸÇFHI Jþ ¢2D

(2)

E#EFTUF-6 \o> HIJ@ƒ„2” Z[ÐnÑr\o>•–—˜™š HIJZ[ `nØrhi \o>•–—˜™š HIJZ[#G EFH$Refghi jkklmf Z[ nXVopqr‘›‘IZ[ˆ‰Êk}~€ 2yz#; 6ÎGJKLMEF2HIJZ [#;         !"#$%&' t-HIJZ[N‰,ƒ„ fuzzy ARTMAP2‘›[HIJÆ¢Ç,-O|[ HIJ¿ïPÇQRxˆ‰)STUV ƒ„AWúˆ‰X.YCZ[\/2 ƒ „ ` Ê 8 # ð    2 ]z K ^  t -ART-based HIJZ[,_>`Hœ 34# ART-basedHIJZ[ a’£R STƒ„d\HIJ2bÝÆÇH£RÎ b2cLÐn1rt-ƒ„ EFdˆ‰‚Š“ -†‡iefinvigilance parameterr#5Ç2 ƒ„ ghQR‡-†‡-ˆiAWjk×n2r t-Z[2}~&'(,lmn62#EF2&' (}~“È}~ùoP2éê,ph. À2ù6EFùˆ2}~‡phqrp hn6# t-HIJZ[ EF`0st ú2Z[Ðn1rART-based HIJZ[ `(2)hi ART-based HIJZ[(u! ùv)#tst ART-based HIJZ[H IJƒ„¢,wx.)}~`yì{#RÎ búÆLÐn1rFWzHIJ2zâB úW,Iò){W,J{LM2 hi×n2r{2òúW|} ~`~€){WàW ’2^ ‚iC?8i×n3r{W ˆ‰G ^‚iC?8iƒ)WàúˆrT„# ( )*+, t- ART-based HIJƒ„,-…22c d[HIJÇ|}z2††âB2 UnC outstarr2u“ùv5 z2† †âB2‡0“- ART-based HIJ)U 2à,“- outstar HIJ#z2ûˆ‰Š‹ zjk)ߊGt-jkŒP††âB2 # ††âB2ˆ‰ !ð}~âB íîU† †âB#††âB2 2IJ3-‡,x}~2 {#)ŽŽO)tuIJ3 á !x“ -††âBù6EFtuIJ3@††âB IJ3#GEF¿¤G††âB2@“-MAXNETq@$>‘’“-zâB%ûQR “-††âBIJ3Q “Ý)5Ç2††IJ 3àúT„#$”-††IJ3)*%•R“– —>††IJ3z2€2˜™EFtt –˜™@`H˜™ÇN‰,ˆ‰š›zâB` H2„# ††âB2 EFHœ“-efžŸ žKš}~{#t-i2Ê8GQ Pƒ„ U2Ê8`2éêÍÊ2efžiQ ƒ„UÍ82HIJs.$¡0ÍË2 ¢Ù×&ÆàQƒ„UÍÊ2HIJÈÍ 2¢Ù# U2à,-~£Lj‰$ùR2††â BIJ3~0?—3‡2F3~)t-?8 —3‡ýõöU2 ùˆIJ32-‡#”“-††âBIJ32F3~, !ÇU22 ùRIJ3ƀ2˜™ #i.ù?‘’“-zâBûQ“݆†âB2 2“-††âB IJ3ãäGÇù_2F3~iU2ŒU ¤t-U2F3~ý,zâB2~# -( ./0!"12 t- ART-based HIJZ[2}~& '(,“-F¥œ2cd}~&'(¦“-} ~§¥ EFA“-pš›œ ART }~&'( K}~ ART HIJ×)¦F-}~§¥  E F A  counter-propagation } ~ & ' ( 2 GrossbergÛÜ(àK}~U2# 3(  4506!"12 ‰G“¨ EF-ˆG ~††âB~€`^‚iC?8i nr“©,hi ART-based HIJZ[ ªRr«P ¬#­.EFG~ ®¯††âB~€ ãîU5.$2††â B$> ART-based HIJZ[)*t

(3)

-††âBý, {2ò×s$>hi ART-basedHIJZ[)*ÇHˆ‰“ -°2±)²ï,G^‚iC³8iP .ù$®U2††âB #      (789:;<=>?@A;#$ ðRefghijkklmfHIJZ [nFSRVQr EF봉oP“-[éê› c2HIJ[Ÿˆ‰µ¶Êk2€} ~t-HIJ¢#)äEFûÝA·¸ ¹B(KóP“-Íd\2HIJƒ„²º ùˆ2}~€à,0»‡0OttvQ¡ 0Çþ¥¢2¼½# @’ /}~€2NOEF$5ƒ„u 2‘›#)EF2‘›Z[ EF6-8 2HIJ[ŸKîõIR2²-ÊHIJ [ŸãäGIZ[ 2Refg`HŸ‘ ¾¿>HIJ[ŸÆ”-`Hfg%£ RS2HIJ[Ÿ5ƒ„u 2 ùv# EFÆù6Qr2‘›5IqÎÐn1r $>}~âB)tÇFvûˆ¤}~“ÀÍ8 ƒ„2HIJùˆ2}~€GQÊÁ 8×n2ri>EFGIʃ„2HIJ[Ÿ `0-8HIJ[ŸEF¤6Gtu 82HIJ`¯ ÃÄÅ¢¤ } ~tAoEF2‘›Z[ùˆ2}~€ Æ8 >IZ[×n3r$>’/ŠHIJƒ„) *EF2Ž(w,A·¸¹B(s,$>8 ƒ„2HIJ‰Êƒ„2HIJÍÔ ¡[Ud\2HIJƒ„×n4rEF2Z [ áÇ2HIJ[Ÿ%ûˆ‰LMåu Ïø.À2zjkEF¤6Í82[ŸK Y[U[âB# ‘› FSRVQ Z[,- §ƒ„¦ “2@Refg`HŸ¦F2,-8ƒ„H IJ[Ÿ)ŠÎK22à,hijk ~€#‘›Z[ÈIZ[.hijk~€ 22‡`~€2Ê8,iAWSŠ2‡È C~€ÈÊàéêÈs¿¡0ùˆ2 ~—3‡O# t-‘› FSRVQ Z[ EFÉ“ - … ¥ œ 2 c d [ Û Ü & ' (  5 | } backpropagationÛÜ(àGEF¿G.ù TU2 ART-based Z[ 2ÛÜ&'(| } #¦“-}~§¥ EF­.A‘› {2 ART ÛÜ(àK}~¦“2ƒ„ 2Re fg`HŸ#¦F-}~§¥ EFàA backpropagationÛÜ(àK}~¦F2ƒ„ 2HIJ[ŸÀ–#ʊx2§¥ E FËÖA ART ÛÜ(àK}~hijk~€#

u 3 ÌâBu BCDEFGH é7ÍYþÎ EFAÏÐ 512 512Ñ 256 Ò§2"Lena""Baboon""Jet" "Pepper"ÓÔâBunu…ùvrK„@ÍY ÌãAÕӁnPSNR,dBr`nCR, bits/pixelr„@ÍYŽœ# (   !"#$ $"Lena"tÐâBuùoP2é7ÍY ö 1 `ö 2 ùv)5Ç2âBu2éê` Çö 345 ùv# ðþÎ EF¤6Ö×UÈÊ2efž iQV„UÍÊ2HIJ)ä¿TÒÕ Óst¿¡0ÍË2#€ ¢EF2&'(ghûˆ‰“}~ØÙý¤ €‚Æ8>5ÇÏ-Ú2HIJZ[#

(4)

PSNRdB CRb/p SOFM 29.02 1.75 AMLP 28.40 1.0 PHNN 29.22 1.03 SHNN 31.10 1.75  1 ”Lena”SOFMAMLP  PHNN SHNN Vigilance ρ # of subnets PSRN (dB) CRv(b/p) 0.300 57 25.60 0.65 0.325 89 25.88 0.73 0.350 145 26.67 0.82 0.375 225 27.07 0.85 0.400 357 28.10 0.98 0.425 645 28.76 1.18 0.450 1278 30.94 1.56  2 ”Lena”ART-based     Vigilance ρ # of subnets PSRN (dB) CRv(b/p) 0.300 113 19.28 0.74 0.325 208 19.88 0.85 0.350 377 20.42 0.99 0.375 644 20.77 1.18 0.400 1066 21.24 1.45 0.425 1631 21.96 1.73 0.450 2435 23.87 2.19  2 ”Baboon”ART-based    Vigilance ρ # of subnets PSRN (dB) CRv(b/p) 0.300 79 24.52 0.72 0.325 122 25.47 0.75 0.350 182 26.23 0.83 0.375 296 26.42 0.88 0.400 459 27.72 1.03 0.425 750 28.97 1.24 0.450 1333 31.09 1.58  2 ”Jet”ART-based     Vigilance ρ # of subnets PSRN (dB) CRv(b/p) 0.300 76 25.12 0.72 0.325 108 25.68 0.74 0.350 162 25.80 0.82 0.375 247 26.85 0.87 0.400 366 27.99 0.99 0.425 634 29.22 1.18 0.450 1299 30.76 1.57  2 ”Pepper”ART-based    78 IJKL )*#$ $"Lena"tÐâBuùoP2é7ÍY ö 6 ùv#i>EFGIʃ„2HIJ[ Ÿ`0-82HIJ[ŸEFùˆ2 }~€Û,I FSRVQ Z[2Ü`Ɠݯ  ý¤sEFéê¢ÞúI FSRVQ Z[# ¡0r“ßê2Iq>‘› FSRVQ HIJZ[ 2áÇ2HIJ[Ÿ…8àÇ2ƒ„ áÊ2âàQAéêãÒs¿äÊ}~ €2|Ö# EFA §~Žœ$tstHIJ Z[2éêQRÊ2TãstÞ$ I FSRVQ Z[)*ÇGµ¶  2}~€ )EF2Z[à Í8u# EFGtu82HIJ`¯à ÄÅ¢¤}~á-HIJ[ŸåSŠ æ}~tQAoEF2‘›Z[ùˆ2}~ PSNR (dB) CR (b/p) FSRVQ 30.65 0.212  FSRVQ 29.41 0.212  6 ”Lena”FSRVQ !" FSRVQ   MGN EFTU2HIJZ[,“tn6 2HIJ&'(+TU2-HIJ [nç )äEF2Ž(Ô¡Y ƒ„CݯúˆH.YCƒ„ù6EF2 Ž(+TU2Ï-HIJZ[ c  þ# OPQR

1. M. A. Abidi, S. Yasuki and P. B. Crilly, Image compression using hybrid neural networks combining the auto-associative multi-layer perceptron and the self-organizing feature map. IEEE Trans. On Consumer Electronics. 40(4): 796-811, November.

2. S. A. Rizvi, N. M. Nasrabadi, Finite-state residual vector quanization using a tree-structured competitive neural network, IEEE Trans. On Circuits and Systems for Video Technology, 7(2): 377-390, 1997.

3. G. W. Cottrell and P. Munro, Principal components analysis of Images via backpropagation. SPICE vol. 10011, Visual Communication and Image Processing, pages:1070-1077, 1988.

4. N. M. Nasrabadi and R. A. Feng. Vector quantization of Images based upon Konhnen self-organizing feature map. IEEE Int. Conf. On Nerual Networks, 1:101-108, 1988.

5. M. Mougeot, R. Azencott, and B. Angeniol. Image compression with backpropagation: Improvement of the visual restoration different cost functions. Neural Networks, 4:467-476, 1991.

6. H. L. Tsai, S. H. S un, and S. J. Lee. Image compression using ART-based neural networks. In proceedings of National Computer Symposium, 1:B163-B168, Taichung, Tainwan, 1997.

參考文獻

相關文件

Wang, A recurrent neural network for solving nonlinear convex programs subject to linear constraints, IEEE Transactions on Neural Networks, vol..

In this paper, we build a new class of neural networks based on the smoothing method for NCP introduced by Haddou and Maheux [18] using some family F of smoothing functions.

SG is simple and effective, but sometimes not robust (e.g., selecting the learning rate may be difficult) Is it possible to consider other methods.. In this work, we investigate

{ Title: Using neural networks to forecast the systematic risk..

Ongoing Projects in Image/Video Analytics with Deep Convolutional Neural Networks. § Goal – Devise effective and efficient learning methods for scalable visual analytic

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Performance of technical analysis in growth and small

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Performance of technical analysis in growth and small

They are suitable for different types of problems While deep learning is hot, it’s not always better than other learning methods.. For example, fully-connected