{"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. Kompressioonialgoritm","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Tere j\u00e4lle! Leidsin selle artikli, mis on kirjutatud juba 2019. aasta mais. See on osa artiklite sarjast WAVE ja JPEG kohta, esmalt<\/a><\/noindex>. See postitus sisaldab teavet pildikoodeerimise algoritmi ja formaadi enda kohta.<\/p>\n

<\/p>\n

Natuke ajalugu<\/h3>\n

<\/p>\n

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

<\/p>\n

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

See standard loodi fotospetsialistide \u00fchendatud grupi poolt 1991. aastal, et tagada piltide efektiivne tihendamine.<\/p>\n

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

Millised on sammud, mille pildid l\u00e4bivad toorelt JPEG-ini.<\/h3>\n

<\/p>\n

M\u00f5ned arvavad, et JPEG-pildid on Huffmani meetodil tihendatud toored andmed, kuid see pole t\u00f5si. Enne itsetihedust l\u00e4bivad andmed pika tee.<\/p>\n

<\/p>\n

Esimene samm on v\u00e4rvimudeli muutmine RGB-st YCbCr-ks. Selle jaoks on isegi spetsiaalne algoritm \u2014 siin<\/a><\/noindex>. Y-d ei muudetud, kuna see vastutab heleduse eest ja selle muutmine oleks kohe m\u00e4rgatav.<\/p>\n

<\/p>\n

Esimene, mida pildiga tehakse, on \u201eharvendamine\u201c<\/strong> (subsampling). Seda on lihtne m\u00f5ista: v\u00f5tame 2x2 pikslite massiivi, seej\u00e4rel v\u00f5tame Cb ja Cr \u2014 iga YCbCr komponendi keskmised v\u00e4\u00e4rtused nendest neljast pikslitest. Nii saime me 6 baiti kokkuhoiust<\/em>, asemel 4 Y, 4 Cb, 4 Cr saime 4 Y ja igale neist sama Cb ja Cr (4 + 4 + 4 = 12; 4 + 1 + 1 = 6; 12 \u2014 6 = 6). Kahe 2 x 2 m\u00f5\u00f5tme puhul, kaotusega, compression ratio 2:1 k\u00f5lab see h\u00e4sti. See kehtib kogu pildi kohta. Nii et - pooled suurusest on kadunud. Ja seda tehnikat saame kasutada meie v\u00e4rvitaju t\u00f5ttu. Inimene m\u00e4rkab kergesti heleduse erinevust, kuid mitte v\u00e4rvi, kui see on keskmiseks v\u00e4ikestes pikslite blokkides. Samuti saab harvendamine toimuda rida-rida, 4 pikslit horisontaalselt ja vertikaalselt. Esimene variant on sagedamini kasutusel. Kui pildi kvaliteet on oluline, siis harvendamine ei toimu \u00fcldse.
\nVisuaalne illustreerimine proportsioneerimisest (Habr ei lubanud gif'i lisada) \u2014 https:\/\/i.ibb.co\/Rg5Th9H\/150953010617579181.gif<\/a><\/noindex><\/p>\n

<\/p>\n

Peamine ettevalmistus<\/h5>\n

\nDCT<\/p>\n

N\u00fc\u00fcd k\u00f5ige keerulisem ja vajalik osa. Kogu pilt jagatakse 8 x 8 plokkideks (kasutatakse t\u00e4itmist, kui eraldusv\u00f5ime ei ole ploki k\u00fclje suurusega v\u00f5rdeliselt).<\/p>\n

<\/p>\n

N\u00fc\u00fcd rakendatakse igale plokile DCT (Diskreetkosinusmuundur).<\/strong>. Selles osas eemaldatakse pildist k\u00f5ik liigne. Kasutades DCT-d tuleb m\u00f5ista, kas antud blokk (8 x 8) kirjeldab midagi monotoonset pildist: taevast, seina; v\u00f5i sisaldab see keerulist struktuuri (juuksed, s\u00fcmbolid jne). On loogiline, et 64 sarnase v\u00e4rviga piksli saab kirjeldada vaid \u00fchega, kuna ploki suurus on juba teada. Siin on teie kompressioon: 64 kuni 1.<\/p>\n

<\/p>\n

DCT muudab ploki spektriks ja seal, kus lugemised j\u00e4rsult muutuvad, muutub koefitsient positiivseks, ja mida j\u00e4rsem on \u00fcleminek, seda k\u00f5rgem on v\u00e4ljund. Seal, kus koefitsient on k\u00f5rgem, on pildil selged \u00fcleminekud v\u00e4rvis ja heleduses, kus see on madalam \u2014 n\u00f5rgad (siledad) muutused YCbCr komponentide v\u00e4\u00e4rtustes plokis.<\/p>\n

\nKvantimine<\/p>\n

Siin rakendatakse juba kompressiooni seadistusi. Iga koefitsient igas 8 x 8 maatriksis jagatakse teatud arvuga. Kui te ei kavatse p\u00e4rast k\u00f5iki selle modifikatsioone kvaliteeti enam v\u00e4hendada, siis peab jagaja olema \u00fcks. Kui teie jaoks on olulisem selle pildi ehk foto m\u00e4lu, siis jagaja on suurem kui 1 ja kvoti \u00fcmardatakse. Nii juhtub, et p\u00e4rast \u00fcmardamist saab sageli palju nullide arvu.<\/p>\n

<\/p>\n

Kvantimine tehakse, et luua veelgi suurema kompressimise v\u00f5imalus. Nii see v\u00e4lja n\u00e4eb, v\u00f5ttes arvesse kvantimist funktsioonil y = sin(x):<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

\nKompressioon<\/p>\n

Esmalt liigume maatriksis nagu ziku-zakuga:<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

<\/p>\n

Saame \u00fchem\u00f5\u00f5tmelise massiiviga, kus on numbrid. N\u00e4eme, et seal on palju nullide, mida saab eemaldada. Selleks asendame j\u00e4rjestuse paljusest nullist 1 nulliga ja p\u00e4rast seda numbriga, mis t\u00e4histab nende arvu j\u00e4rjestuses. Nii saab kogu massiivi suurust v\u00e4hendada kuni 1\/3. Ja siis kompressitakse neid massiivi Haffmani meetodil ja kirjutame juba faili.<\/p>\n

<\/p>\n

Kus kasutatakse<\/h3>\n

<\/p>\n

K\u00f5ikjal. Nii nagu PNG, kasutatakse JPEG-i ka kaamerates, OS-ides (ettev\u00f5tte logo, rakenduse ikoonide, miniatuuri jne koha peal) ja igas v\u00f5imalikus valdkonnas, kus on vaja t\u00f5husalt pilte hoida.<\/p>\n

<\/p>\n

Kokkuv\u00f5te<\/h3>\n

<\/p>\n

Praegu on teadmised JPEG-i kohta v\u00e4\u00e4rtuslikud vaid hariduse seisukohalt, kuna see on juba igale poole integreeritud ja suured r\u00fchmad inimesi on seda optimeerinud, kuid teaduse kivi on siiski maitsev.<\/p>\n

\nAllikad<\/p>\n

Artikkel YCbCr-ist Vikipeedias<\/a><\/noindex>
\nArtikkel JPEG-ist Vikipeedias<\/a><\/noindex>
\nVeidi DCT-st postitusest Pikabu-st<\/a><\/noindex>
\nArtikkel DCT-st Vikipeedias<\/a><\/noindex><\/p>\n

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

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