Hardware Implementation of JPEG-LS Codec

Total Page:16

File Type:pdf, Size:1020Kb

Hardware Implementation of JPEG-LS Codec Rochester Institute of Technology RIT Scholar Works Theses 9-1-2001 Hardware Implementation of JPEG-LS codec Michael Piorun Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Piorun, Michael, "Hardware Implementation of JPEG-LS codec" (2001). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Hardware Implementation of a JPEG-LS Codec ! "#! ! Michael Piorun $!%&'()(!*+",)--'.! )/! 012-)13!4+35)33,'/-!65!-&'! 7'8+)2','/-(!562!-&'!9':2''!65! ;$*%<7!=4!*>?<@><! )/! >6,A+-'2!</:)/''2)/:! ! ! ! ! ! ! ! ! ! ! $AA26B'.!"#C! ! ! Principal AdvisorDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD! $/.2'1(!<E!*1B1F)(G!$((6H)1-'!0265'((62!1/.!9'A12-,'/-!I'1.! ! Committee MemberDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD! 76#!*E!>J'2/)F6K(F)G!0265'((62! ! Committee MemberDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD! L'//'-&!ME!I(+G!0265'((62! ! 9'A12-,'/-!65!>6,A+-'2!</:)/''2)/:! >633':'!65!</:)/''2)/:! 76H&'(-'2!?/(-)-+-'!65!%'H&/636:#! 76H&'(-'2G!@'K!N62F! *'A-',"'2!OPPQ! RELEASE PERMISSION FORM ! Rochester Institute of Technology Hardware Implementation of a JPEG-LS Codec! ! ! ! ! ?G!;)H&1'3!0)62+/G!&'2'"#!:21/-!A'2,)(()6/!-6!-&'!M1331H'!R)"212#!1/.!-&'!9'A12-,'/-!65! >6,A+-'2!</:)/''2)/:!1-!-&'!76H&'(-'2!?/(-)-+-'!65!%'H&/636:#!-6!2'A26.+H'!,#!-&'()(!)/!K&63'! 62!)/!A12-E!!$/#!2'A26.+H-)6/!K)33!/6-!"'!562!H6,,'2H)13!+('!62!A265)-E! ! ! ! ! ! ! ______________________________________________ ;)H&1'3!9E!0)62+/! ! ! DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD! 91-'! ! ! Abstract ! %&'!A2),12#!:613!65!-&)(!-&'()(!)(!-6!),A3','/-!1!&12.K12'!B'2()6/!65!-&'!S0<TUR*G!62!S0<TU R6((3'((G! ),1:'! H6,A2'(()6/! 13:62)-&,! )/! VI9RE! ! %&'! S0<TUR*! 13:62)-&,! )(! H+22'/-3#! -&'! .'():/1-'.! (-1/.12.! 562! 36((3'((! H6,A2'(()6/! 65! :21#(H13'! 1/.! H6362! ),1:'(! "#! -&'! S0<T! H6,,)--''E! ! $3-&6+:&! 36((#! ),1:'! H6,A2'(()6/! )(! K).'3#! +('.! K&'/! .'13)/:! K)-&! :21#(H13'! ),1:'(G!-&'2'!12'!(6,'!1AA3)H1-)6/(!-&1-!2'8+)2'!36((3'((!),1:'!H6,A2'(()6/!(6!-&1-!-&'!62):)/13! ),1:'!,1#!"'!2'H6B'2'.E!!%&)(!)(!65-'/!-&'!H1('!562!&)(-62)H13!1/.!3':13!.6H+,'/-!),1:'!12H&)B'(G! ,'.)H13!1/.!(1-'33)-'!),1:'2#G!1/.!")6,'-2)H!),1:'(E!!! ! %&'!S0<TUR*!13:62)-&,!)(!,+H&!3'((!H6,A3'W!-&1/!6-&'2!H+22'/-!36((3'((!),1:'!H6,A2'(()6/! 13:62)-&,(! 1/.! 655'2(! (),)312! 62! "'--'2! H6,A2'(()6/! :1)/(E! ! @'12U36((3'((! H6,A2'(()6/! 655'2(! &):&'2!H6,A2'(()6/!:1)/(!"#!+()/:!1!A)W'3!-63'21/H'!(A'H)5)'.!"#!-&'!+('2E!!!!%&'!13:62)-&,!+('(!1! A2'.)H-)B'!-'H&/)8+'!562!H6,A2'(()6/G!1/.!-&'!2'(+3-)/:!A2'.)H-)6/!'2262!)(!'/H6.'.G!/6-!-&'!A)W'3! B13+'!)-('35E!!%&)(!A2'.)H-)6/!'2262!)(!'/H6.'.!K)-&!T636,"U7)H'!H6.)/:G!K&)H&!)(!6A-),13!562!1! :'6,'-2)H!.)(-2)"+-)6/!(+H&!1(!A2'.)H-)6/!'2262E!!%&'!A2'.)H-62!'/-'2(!1!(A'H)13!2+/U3'/:-&!,6.'!-6! '/H6.'!A)W'3(!K)-&!).'/-)H13!B13+'(!)/!36((3'((!,6.'!X62!/'123#!).'/-)H13!B13+'(!K)-&)/!1!F/6K/! B13+'!)/!/'12U36((3'((!,6.'YG!K&)H&!,1W),)J'(!H6,A2'(()6/!5+2-&'2E!!! ! ?/!-&)(!-&'()(G!-&'!S0<TUR*!13:62)-&,!)(!),A3','/-'.!)/!>G!VI9RG!1/.!5+2-&'2!(#/-&'()J'.!+()/:! -&'!*#/6A(#(!(#/-&'()(!-663!(+)-'E!!0)H-62)13G!.6H+,'/-G!,'.)H13G!2',6-'!('/()/:G!1/.!")6,'-2)H! ),1:'(! 12'! +('.! 562! -'(-)/:! -&'! A26Z'H-! 1:1)/(-! 1/6-&'2! (-1/.12.UH6,A3)1/-! (65-K12'! ),A3','/-1-)6/E! ! %&'! H6,A2'(()6/! 21-)6! 562! 36((3'((! H6,A2'(()6/! )(! 1AA26W),1-'3#! O! 1/.! )(! :2'1-'2!562!/'12U36((3'((!H6,A2'(()6/E!!%&'!'/.!2'(+3-!)(!1!*#/6A(#(!(H&',1-)H!-&1-!2'A2'('/-(!1! S0<TUR*! H6.'HG! K&)H&! )(! H1A1"3'! 65! 36((3'((! 1/.! /'12U36((3'((! '/H6.)/:! 1/.! .'H6.)/:E!!! 0'2562,1/H'!H&121H-'2)(-)H(!(+H&!1(!H&)A!12'1G!(A''.G!1/.!A6K'2!H6/(+,A-)6/!12'!'W-21H-'.!526,! -&'! (#/-&'()(! -663E! %&'('! 12'! 1AA26W),1-'3#! [\]GPPP! :1-'(G! 1! Q]! /(! H36HF! H#H3'G! 1/.! ]^! ,M! 2'(A'H-)B'3#E!!$!&12.K12'!),A3','/-1-)6/!65!-&)(!13:62)-&,!6/!1/!40T$!62!$*?>!K6+3.!:)B'!1! .):)-13!H1,'21!62!(H1//'2!1/!'.:'!)/!-&'!,12F'-A31H'E! Acknowledgements %&'!1+-&62!K6+3.!3)F'!-6!1HF/6K3'.:'!-&'!,',"'2(!65!-&'!-&'()(!H6,,)--''!562!-&')2!:+).1/H'! 1/.!1(()(-1/H'!K)-&!-&'!A26Z'H-C!!92E!$/.2'1(!*1B1F)(G!92E!76#!>J'2/)F6K(F)G!1/.!92E!L'/!I(+G! 1/.!13(6!51,)3#!1/.!52)'/.(!562!-&')2!(+AA62-E !)) Table of Contents ! List of Figures...................................................................................................................vi! List of Tables....................................................................................................................vii! Introduction ....................................................................................................................... 2! 1! Information Coding Theory .....................................................................................4! 1.1! Entropy................................................................................................................ 4! 1.2! Huffman Coding .................................................................................................. 6! 1.3! Arithmetic Coding ............................................................................................... 8! 1.4! Golomb-Rice Coding......................................................................................... 10! 1.5! Run-Length Coding ........................................................................................... 12! 2! Image Compression Theory ................................................................................... 13! 2.1! Image Compression Background ...................................................................... 13! OEQEQ! %#A'(!65!>6,A2'(()6/EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q[! OEQEQEQ! R6((#!>6,A2'(()6/ EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q_! OEQEQEO! R6((3'((!>6,A2'(()6/ EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q_! OEQEQE[! @'12UR6((3'((!>6,A2'(()6/ EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q]! OEQEO! %21/(562,!>6.)/: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q]! OEQEOEQ! L12&+/'/UR6`B'!%21/(562,!XLR%Y EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Qa! OEQEOEO! 9)(H2'-'!>6()/'!%21/(562,!X9>%YEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Qa! OEQEOE[! 9)(H2'-'!M1B'3'-!%21/(562,!X9M%YEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Qa! OEQE[! 02'.)H-)6/Ub1('.!>6.)/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Q\! OEQE[EQ! R6((#!02'.)H-)B'!>6.)/: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Qc! OEQE[EO! R6((3'((!02'.)H-)B'!>6.)/: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE OP! OEQE_! b)-!031/'!</H6.)/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE OP! OEQE]! R6((#U+A6/UR6((3'((!;'-&6.!562!@'12UR6((3'((!>6.)/: EEEEEEEEEEEEEEEEEEEEEEEEE OO! 2.2! Image Compression Algorithms........................................................................ 23! OEOEQ! S0<T!>6,A2'(()6/!;'-&6. EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE O[! OEOEQEQ! b1('3)/'!S0<T!;'-&6.EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE O[! OEOEQEO! R6((3'((!S0<T!;'-&6.EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE O_! OEOEO! T?4!1/.!0@TEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE O_! OEOE[! >$R?>C!>6/-'W-U"1('.!$.1A-)B'!R6((3'((!?,1:'!>6.'H EEEEEEEEEEEEEEEEEEEEEEEEE O]! OEOE_! 4<R?>*C!41(-!<55)H)'/-!R6((3'((!?,1:'!>6,A2'(()6/!*#(-', EEEEEEEEEEEEEEEEE Oa! OEOE]! *d0!%21/(562, EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE O\! OEOEa! Sb?T!e!Sb?TOEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE Oc! OEOE\! S0<T!OPPPEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [P! OEOEc! =-&'2!>6,A2'(()6/!$3:62)-&,( EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [Q! !))) OEOE^! >6,A12)(6/!65!R6((3'((!>6,A2'(()6/!$3:62)-&,(!-6!S0<TUR*EEEEEEEEEEEEEEE[Q! 3! Design of the JPEG-LS Encoder and Decoder ..................................................... 33! 3.1! Algorithm Description....................................................................................... 33! [EQEQ! =B'2B)'K EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [[! [EQEO! >6/-'W-!;6.'3)/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [_! [EQE[! 7':+312!;6.'!026H'(()/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [\! [EQE[EQ! 02'.)H-62 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [\! [EQE[EO! 02'.)H-)6/!<2262!;1AA)/:!1/.!</H6.)/: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [c! [EQE[E[! >6/-'W-!V12)1"3'(!fA.1-' EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE [^! [EQE_! 7+/!;6.'!026H'(()/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE _P! [EQE_EQ! 7+/!*H1//)/:!1/.!7+/U3'/:-&!>6.)/:EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE_P! [EQE_EO! 7+/!?/-'22+A-)6/!>6.)/: EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE_Q! [EQE]!
Recommended publications
  • Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
    Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1
    [Show full text]
  • How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
    How to Exploit the Transferability of Learned Image Compression to Conventional Codecs Jan P. Klopp Keng-Chi Liu National Taiwan University Taiwan AI Labs [email protected] [email protected] Liang-Gee Chen Shao-Yi Chien National Taiwan University [email protected] [email protected] Abstract Lossy compression optimises the objective Lossy image compression is often limited by the sim- L = R + λD (1) plicity of the chosen loss measure. Recent research sug- gests that generative adversarial networks have the ability where R and D stand for rate and distortion, respectively, to overcome this limitation and serve as a multi-modal loss, and λ controls their weight relative to each other. In prac- especially for textures. Together with learned image com- tice, computational efficiency is another constraint as at pression, these two techniques can be used to great effect least the decoder needs to process high resolutions in real- when relaxing the commonly employed tight measures of time under a limited power envelope, typically necessitating distortion. However, convolutional neural network-based dedicated hardware implementations. Requirements for the algorithms have a large computational footprint. Ideally, encoder are more relaxed, often allowing even offline en- an existing conventional codec should stay in place, ensur- coding without demanding real-time capability. ing faster adoption and adherence to a balanced computa- Recent research has developed along two lines: evolu- tional envelope. tion of exiting coding technologies, such as H264 [41] or As a possible avenue to this goal, we propose and investi- H265 [35], culminating in the most recent AV1 codec, on gate how learned image coding can be used as a surrogate the one hand.
    [Show full text]
  • Annual Report 2016
    ANNUAL REPORT 2016 PUNJABI UNIVERSITY, PATIALA © Punjabi University, Patiala (Established under Punjab Act No. 35 of 1961) Editor Dr. Shivani Thakar Asst. Professor (English) Department of Distance Education, Punjabi University, Patiala Laser Type Setting : Kakkar Computer, N.K. Road, Patiala Published by Dr. Manjit Singh Nijjar, Registrar, Punjabi University, Patiala and Printed at Kakkar Computer, Patiala :{Bhtof;Nh X[Bh nk;k wjbk ñ Ò uT[gd/ Ò ftfdnk thukoh sK goT[gekoh Ò iK gzu ok;h sK shoE tk;h Ò ñ Ò x[zxo{ tki? i/ wB[ bkr? Ò sT[ iw[ ejk eo/ w' f;T[ nkr? Ò ñ Ò ojkT[.. nk; fBok;h sT[ ;zfBnk;h Ò iK is[ i'rh sK ekfJnk G'rh Ò ò Ò dfJnk fdrzpo[ d/j phukoh Ò nkfg wo? ntok Bj wkoh Ò ó Ò J/e[ s{ j'fo t/; pj[s/o/.. BkBe[ ikD? u'i B s/o/ Ò ô Ò òõ Ò (;qh r[o{ rqzE ;kfjp, gzBk óôù) English Translation of University Dhuni True learning induces in the mind service of mankind. One subduing the five passions has truly taken abode at holy bathing-spots (1) The mind attuned to the infinite is the true singing of ankle-bells in ritual dances. With this how dare Yama intimidate me in the hereafter ? (Pause 1) One renouncing desire is the true Sanayasi. From continence comes true joy of living in the body (2) One contemplating to subdue the flesh is the truly Compassionate Jain ascetic. Such a one subduing the self, forbears harming others. (3) Thou Lord, art one and Sole.
    [Show full text]
  • Chapter 9 Image Compression Standards
    Fundamentals of Multimedia, Chapter 9 Chapter 9 Image Compression Standards 9.1 The JPEG Standard 9.2 The JPEG2000 Standard 9.3 The JPEG-LS Standard 9.4 Bi-level Image Compression Standards 9.5 Further Exploration 1 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 9.1 The JPEG Standard JPEG is an image compression standard that was developed • by the “Joint Photographic Experts Group”. JPEG was for- mally accepted as an international standard in 1992. JPEG is a lossy image compression method. It employs a • transform coding method using the DCT (Discrete Cosine Transform). An image is a function of i and j (or conventionally x and y) • in the spatial domain. The 2D DCT is used as one step in JPEG in order to yield a frequency response which is a function F (u, v) in the spatial frequency domain, indexed by two integers u and v. 2 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression The effectiveness of the DCT transform coding method in • JPEG relies on 3 major observations: Observation 1: Useful image contents change relatively slowly across the image, i.e., it is unusual for intensity values to vary widely several times in a small area, for example, within an 8 8 × image block. much of the information in an image is repeated, hence “spa- • tial redundancy”. 3 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression (cont’d) Observation 2: Psychophysical experiments suggest that hu- mans are much less likely to notice the loss of very high spatial frequency components than the loss of lower frequency compo- nents.
    [Show full text]
  • Arithmetic Coding
    Arithmetic Coding Arithmetic coding is the most efficient method to code symbols according to the probability of their occurrence. The average code length corresponds exactly to the possible minimum given by information theory. Deviations which are caused by the bit-resolution of binary code trees do not exist. In contrast to a binary Huffman code tree the arithmetic coding offers a clearly better compression rate. Its implementation is more complex on the other hand. In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a single symbol rather than several separate codewords. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, a fraction n where (0.0 ≤ n < 1.0) Arithmetic coding is a lossless coding technique. There are a few disadvantages of arithmetic coding. One is that the whole codeword must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting the number of symbols to encode within a codeword. There also exist many patents upon arithmetic coding, so the use of some of the algorithms also call upon royalty fees. Arithmetic coding is part of the JPEG data format.
    [Show full text]
  • Image Compression Using Discrete Cosine Transform Method
    Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process.
    [Show full text]
  • Task-Aware Quantization Network for JPEG Image Compression
    Task-Aware Quantization Network for JPEG Image Compression Jinyoung Choi1 and Bohyung Han1 Dept. of ECE & ASRI, Seoul National University, Korea fjin0.choi,[email protected] Abstract. We propose to learn a deep neural network for JPEG im- age compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective func- tion of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approx- imates bitrates using simple network blocks|two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses| two for semantic image understanding and another two for conventional image compression|and demonstrate the effectiveness of our approach to the individual tasks. Keywords: JPEG image compression, adaptive quantization, bitrate approximation. 1 Introduction Image compression is a classical task to reduce the file size of an input image while minimizing the loss of visual quality. This task has two categories|lossy and lossless compression. Lossless compression algorithms preserve the contents of input images perfectly even after compression, but their compression rates are typically low. On the other hand, lossy compression techniques allow the degra- dation of the original images by quantization and reduce the file size significantly compared to lossless counterparts.
    [Show full text]
  • Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian
    mathematics Article Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian Matea Ignatoski 1 , Jonatan Lerga 1,2,* , Ljubiša Stankovi´c 3 and Miloš Dakovi´c 3 1 Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia; [email protected] 2 Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia 3 Faculty of Electrical Engineering, University of Montenegro, Džordža Vašingtona bb, 81000 Podgorica, Montenegro; [email protected] (L.S.); [email protected] (M.D.) * Correspondence: [email protected]; Tel.: +385-51-651-583 Received: 3 June 2020; Accepted: 17 June 2020; Published: 1 July 2020 Abstract: The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal is to answer a question: ”How does the language of a text affect the compression ratio?” The results indicated that the compression ratio is affected by the size of the language alphabet, and size or type of the text. For example, The European Green Deal was compressed by 75.79%, 76.17%, 77.33%, 76.84%, 73.25%, 74.63%, 75.14%, and 74.51% using the LZW algorithm, and by 72.54%, 71.47%, 72.87%, 73.43%, 69.62%, 69.94%, 72.42% and 72% using the arithmetic algorithm for the English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian versions, respectively.
    [Show full text]
  • Why Compression Matters
    Computational thinking for digital technologies: Snapshot 2 PROGRESS OUTCOME 6 Why compression matters Context Sarah has been investigating the concept of compression coding, by looking at the different ways images can be compressed and the trade-offs of using different methods of compression in relation to the size and quality of images. She researches the topic by doing several web searches and produces a report with her findings. Insight 1: Reasons for compressing files I realised that data compression is extremely useful as it reduces the amount of space needed to store files. Computers and storage devices have limited space, so using smaller files allows you to store more files. Compressed files also download more quickly, which saves time – and money, because you pay less for electricity and bandwidth. Compression is also important in terms of HCI (human-computer interaction), because if images or videos are not compressed they take longer to download and, rather than waiting, many users will move on to another website. Insight 2: Representing images in an uncompressed form Most people don’t realise that a computer image is made up of tiny dots of colour, called pixels (short for “picture elements”). Each pixel has components of red, green and blue light and is represented by a binary number. The more bits used to represent each component, the greater the depth of colour in your image. For example, if red is represented by 8 bits, green by 8 bits and blue by 8 bits, 16,777,216 different colours can be represented, as shown below: 8 bits corresponds to 256 possible numbers in binary red x green x blue = 256 x 256 x 256 = 16,777,216 possible colour combinations 1 Insight 3: Lossy compression and human perception There are many different formats for file compression such as JPEG (image compression), MP3 (audio compression) and MPEG (audio and video compression).
    [Show full text]
  • Video Compression and Communications: from Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers
    Video Compression and Communications: From Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers by c L. Hanzo, P.J. Cherriman, J. Streit Department of Electronics and Computer Science, University of Southampton, UK About the Authors Lajos Hanzo (http://www-mobile.ecs.soton.ac.uk) FREng, FIEEE, FIET, DSc received his degree in electronics in 1976 and his doctorate in 1983. During his 30-year career in telecommunications he has held various research and academic posts in Hungary, Germany and the UK. Since 1986 he has been with the School of Electronics and Computer Science, University of Southampton, UK, where he holds the chair in telecom- munications. He has co-authored 15 books on mobile radio communica- tions totalling in excess of 10 000, published about 700 research papers, acted as TPC Chair of IEEE conferences, presented keynote lectures and been awarded a number of distinctions. Currently he is directing an academic research team, working on a range of research projects in the field of wireless multimedia communications sponsored by industry, the Engineering and Physical Sciences Research Council (EPSRC) UK, the European IST Programme and the Mobile Virtual Centre of Excellence (VCE), UK. He is an enthusiastic supporter of industrial and academic liaison and he offers a range of industrial courses. He is also an IEEE Distinguished Lecturer of both the Communications Society and the Vehicular Technology Society (VTS). Since 2005 he has been a Governer of the VTS. For further information on research in progress and associated publications please refer to http://www-mobile.ecs.soton.ac.uk Peter J.
    [Show full text]
  • Website Quality Evaluation Based on Image Compression Techniques
    Volume 7, Issue 2, February 2017 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Website Quality Evaluation based On Image Compression Techniques Algorithm Chandran.M1 Ramani A.V2 MCA Department & SRMV CAS, Coimbatore, HEAD OF CS & SRMV CAS, Coimbatore, Tamilnadu, India Tamilnadu, India DOI: 10.23956/ijarcsse/V7I2/0146 Abstract-Internet is a way of presenting information knowledge the webpages plays a vital role in delivering information and performing to the users. The information and performing delivered by the webpages should not be delayed and should be qualitative. ie. The performance of webpages should be good. Webpage performance can get degraded by many numbers of factors. One such factor is loading of images in the webpages. Image consumes more loading time in webpage and may degrade the performance of the webpage. In order to avoid the degradation of webpage and to improve the performance, this research work implements the concept of optimization of images. Images can be optimized using several factors such as compression, choosing right image formats, optimizing the CSS etc. This research work concentrates on choosing the right image formats. Different image formats have been analyzed and tested in the SRKVCAS webpages. Based on the analysis, the suitable image format is suggested to be get used in the webpage to improve the performance. Keywords: Image, CSS, Image Compression and Lossless I. INTRODUCTION As images continues to be the largest part of webpage content, it is critically important for web developers to take aggressive control of the image sizes and quality in order to deliver a fastest loading and responsive site for the users.
    [Show full text]
  • The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed.
    [Show full text]