{"id":54788,"date":"2020-01-04T00:00:00","date_gmt":"2020-01-03T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/jpeg-algoritm-szhatiya"},"modified":"2020-02-18T14:02:50","modified_gmt":"2020-02-18T11:02:50","slug":"jpeg-algoritm-szhatiya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/administrirovanie\/jpeg-algoritm-szhatiya","title":{"rendered":"JPEG. Tihendamisalgoritm","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Tere j\u00e4lle! Leidsin selle artikli, mis on kirjutatud 2019. aasta maikuus. See on j\u00e4tk seeriale artikleid WAVE ja JPEG kohta, siit esimene<\/a><\/noindex>. See postitus sisaldab teavet pildikodeerimise algoritmi ja formaadi enda kohta.<\/p>\n

<\/p>\n

Veidi ajalugu<\/h3>\n

<\/p>\n

Supilusikat\u00e4is teavet Vikipeediast:<\/p>\n

<\/p>\n

JPEG (Joint Photographic Experts Group) on \u00fcks populaarsemaid rasterskeemide formaate, mida kasutatakse fotode ja sarnaste piltide salvestamiseks.<\/p><\/blockquote>\n

See standard loodi juba 1991. aastal fotograafiaekspertide \u00fchenduse poolt piltide efektiivseks tihendamiseks.<\/p>\n

<\/a><\/noindex><\/p>\n

Millise tee l\u00e4bib pilt toorversioonist JPEG-iks<\/h3>\n

<\/p>\n

M\u00f5ned arvavad, et JPEG-pildid on Huffmani meetodil tihendatud toored andmed, kuid see ei ole t\u00f5si. Andmed l\u00e4bivad enne tihendamist pika tee.<\/p>\n

<\/p>\n

Esiteks muudetakse v\u00e4rvimudel RGB-st YCbCr-ks. Selle jaoks on isegi spetsiaalne algoritm - siit<\/a><\/noindex>. Y-d ei muudeta, kuna see vastutab heleduse eest ja selle muutmine oleks m\u00e4rgatav.<\/p>\n

<\/p>\n

Esimene asi, mida pildiga tehakse, on \u201eharvendamine\u201c<\/strong> (subsampling). On seda lihtne m\u00f5ista: v\u00f5etakse 2x2 pikslite massiiv, seej\u00e4rel v\u00f5etakse Cb ja Cr \u2014 iga YCbCr komponendi keskmised v\u00e4\u00e4rtused nende 4 piksli kohta. Ning nii, me saime 6 baiti<\/em>, selle asemel et 4 Y, 4 Cb, 4 Cr, saime 4 Y ja iga\u00fchele neist sama Cb ja Cr (4 + 4 + 4 = 12; 4 + 1 + 1 = 6; 12 \u2014 6 = 6). Isegi 2x2 ulatuses, kaotusliku kokkusurumise suhtega 2:1, k\u00f5lab see soliidne. See rakendub kogu pildile. Ja nii \u2013 vabastasime poole suurusest. Sellist tehnikat saame kasutada t\u00e4nu meie v\u00e4rvi tajumisele. Inimene m\u00e4rkab h\u00f5lpsasti heleduse erinevust, kuid mitte v\u00e4rvi, kui see on v\u00e4ikese ploki keskmine. Samuti saab harvendamist teha ritta, 4 pikslit horisontaalselt ja vertikaalselt. Esimene variant on kasutusel sagedamini. Kui pildi kvaliteet on oluline, siis harvendamist ei tohiks \u00fcldse teha.
\nIllustratiivne n\u00e4ide vahe tegemisest (Habr ei lubanud gif'i sisestada) \u2014 https:\/\/i.ibb.co\/Rg5Th9H\/150953010617579181.gif<\/a><\/noindex><\/p>\n

<\/p>\n

Valmistamise peamine osa<\/h5>\n

\nDCT<\/p>\n

N\u00fc\u00fcd k\u00f5ige keerulisem ja vajalik osa. Kogu pilt jagatakse plokkideks 8x8 (kasutatakse t\u00e4itmist juhul, kui eraldusv\u00f5ime ei ole ploki k\u00fcljele jagatav).<\/p>\n

<\/p>\n

N\u00fc\u00fcd rakendatakse iga ploki peale DCT (Diskreetne kosinusmuundamine)<\/strong>. Selles osas eemaldatakse pildist k\u00f5ik \u00fcleliigne. Kasutades DCT-d tuleb m\u00f5ista, kas see plokk (8x8) kirjeldab midagi monotoni list pildist: taevast, seina; v\u00f5i sisaldab see keerulist struktuuri (juuksed, s\u00fcmbolid jne). Loogiliselt v\u00f5ib 64 sarnase v\u00e4rviga pikslit kirjeldada vaid 1-ga, kuna ploki suurus on juba teada. Ja siin on teil kokkusurumine: 64 kuni 1.<\/p>\n

<\/p>\n

DCT muundab ploki spektriks, ja seal, kus n\u00e4idud j\u00e4rsult muutuvad, muutub koefitsient positiivseks, ning mida j\u00e4rsem on \u00fcleminek, seda k\u00f5rgem on v\u00e4ljund. Seal, kus koefitsient on k\u00f5rgem, on pildil selged \u00fcleminekud v\u00e4rvuses ja heleduses, kus see on madalam - n\u00f5rgad (sujuvad) muutused YCbCr komponentide suurustes plokis.<\/p>\n

\nKvantimine<\/p>\n

Siin rakendatakse juba kokkusurumise seadeid. Iga koefitsient igas 8x8 maatriksis jagatakse kindla arvuga. Kui te ei kavatse pilti p\u00e4rast selle modifikatsioone enam v\u00e4iksemaks teha, siis peab jagaja olema 1. Kui m\u00e4lu, mida see foto v\u00f5tab, on olulisem, siis on jagaja suurem kui 1 ja jagatis \u00fcmardatakse. Nii juhtub, et p\u00e4rast \u00fcmardamist saadakse sageli palju nullide juurde.<\/p>\n

<\/p>\n

Kvantimine v\u00f5imaldab veelgi suuremat kokkusurumist. Nii see v\u00e4lja n\u00e4eb, kui v\u00f5tame n\u00e4iteks kvantimise y = sin(x) graafiku:<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

\nKompresseerimine<\/p>\n

Esiteks l\u00e4heme l\u00e4bi maatriksi zick-zack'i viisiliselt:<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

<\/p>\n

Saame \u00fchem\u00f5\u00f5tmelise massi numbrid. Nagu n\u00e4eme, on seal palju nullide j\u00e4rjestusi, mida saab k\u00f5rvaldada. Selleks asendame j\u00e4rjestuse mitme nulliga \u00fche nulliga ja p\u00e4rast seda numbriga, mis t\u00e4histab nende kogust j\u00e4rjestuses. Nii saame kogu massi v\u00e4hendada kuni 1\/3 originaalsuurusest. Edasi surume selle massi Hufmani meetodil ja salvestame juba faili.<\/p>\n

<\/p>\n

Kus kasutatakse<\/h3>\n

<\/p>\n

Igal pool. Nii nagu PNG, kasutatakse JPEG-i ka kaamerates, OS-ides (ettev\u00f5tte logode, rakenduste ikoonide, pisipiltide) ja igasugustes valdkondades, kus on vaja pilte t\u00f5husalt salvestada.<\/p>\n

<\/p>\n

Kokkuv\u00f5te<\/h3>\n

<\/p>\n

Praegu on teadmised JPEG-ist v\u00e4\u00e4rtuslikud vaid hariduslikel eesm\u00e4rkidel, kuna see on juba igale poole integreeritud ja optimeeritud suurte inimgruppide poolt, kuid teaduse graniit on siiski ahvatlev.<\/p>\n

\nAllikad<\/p>\n

Artikkel YCbCr kohta Vikipeedias<\/a><\/noindex>
\nArtikkel JPEG kohta Vikipeedias<\/a><\/noindex>
\nVeidi DCT-st Pikabust postist<\/a><\/noindex>
\nArtikkel DCT kohta Vikipeedias<\/a><\/noindex><\/p>\n

Allikas: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0418 \u0441\u043d\u043e\u0432\u0430 \u0437\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435! \u042f \u043d\u0430\u0448\u0435\u043b \u044d\u0442\u0443 \u0441\u0442\u0430\u0442\u044c\u044e, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u0443\u044e \u0435\u0449\u0435 \u043c\u0430\u0435 2019-\u043e\u0433\u043e \u0433\u043e\u0434\u0430. \u042d\u0442\u043e \u2014 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435 \u0441\u0435\u0440\u0438\u0438 \u0441\u0442\u0430\u0442\u0435\u0439 \u043e WAVE \u0438 JPEG, \u0412\u043e\u0442 \u043f\u0435\u0440\u0432\u0430\u044f. \u042d\u0442\u0430 \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u044f \u0432\u043a\u043b\u044e\u0447\u0438\u0442 \u0432 \u0441\u0435\u0431\u0435 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e\u0431 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0435 \u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0438 \u043e \u0441\u0430\u043c\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435 \u0432 \u0446\u0435\u043b\u043e\u043c. \u0429\u0435\u043f\u043e\u0442\u043a\u0443 \u0438\u0441\u0442\u043e\u0440\u0438\u0438 \u0421\u0442\u043e\u043b\u043e\u0432\u0443\u044e \u043b\u043e\u0436\u043a\u0443 \u0441\u0442\u0430\u0442\u044c\u0438 \u0438\u0437 \u0412\u0438\u043a\u0438\u043f\u0435\u0434\u0438\u0438: JPEG (Joint Photographic Experts Group) \u2014 \u043e\u0434\u0438\u043d \u0438\u0437 \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u044b\u0445 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-54788","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47JPEG. 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