{"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\/hr\/blog\/administrirovanie\/jpeg-algoritm-szhatiya","title":{"rendered":"JPEG. Algoritam kompresije","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Ponovno pozdrav! Prona\u0161ao sam ovaj \u010dlanak napisan u svibnju 2019. godine. Ovo je nastavak serije \u010dlanaka o WAVE i JPEG-u. Evo prva<\/a><\/noindex>. Ova publikacija uklju\u010duje informacije o algoritmu kodiranja slika i o samom formatu u cjelini.<\/p>\n

<\/p>\n

Malo povijesti<\/h3>\n

<\/p>\n

Jedna \u017elica \u010dlanka s Wikipedije:<\/p>\n

<\/p>\n

JPEG (Joint Photographic Experts Group) je jedan od popularnih bitmap formata, koristi se za pohranu fotografija i sli\u010dnih slika.<\/p><\/blockquote>\n

Ova norma je razvijena od strane Udru\u017eene grupe stru\u010dnjaka za fotografiju jo\u0161 1991. godine za u\u010dinkovito komprimiranje slika.<\/p>\n

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

Kakav put prolaze slike od sirovog stanja do JPEG-a<\/h3>\n

<\/p>\n

Neki smatraju da su JPEG slike sirovi podaci komprimirani metodom Huffman, no to nije to\u010dno. Prije kona\u010dne kompresije, podaci prolaze dug put.<\/p>\n

<\/p>\n

Prvo, promjena modela boje se vr\u0161i s RGB na YCbCr. Za to postoji poseban algoritam \u2014 ovdje<\/a><\/noindex>. Y se ne dira, jer on odgovara za svjetlinu i njegovo mijenjanje bi bilo primjetno.<\/p>\n

<\/p>\n

Prva stvar koja se radi sa slikom je \u00abprore\u0111ivanje\u00bb<\/strong> (smanjenje uzorka). To je lako razumjeti: uzima se 2x2 matrica piksela, zatim se uzimaju Cb i Cr \u2014 srednje vrijednosti svake od komponenata YCbCr ovih 4 piksela. I tako, dobijamo 6 bajtova<\/em>, umjesto 4 Y, 4 Cb, 4 Cr dobili smo 4 Y i jednake za svaki od njih Cb i Cr (4 + 4 + 4 = 12; 4 + 1 + 1 = 6; 12 \u2014 6 = 6). U mjerilu \u010dak 2\u00d72 kompresija s gubitkom s koeficijentom kompresije 2:1 zvu\u010di solidno. To se primjenjuje na cijelu sliku. I tako - smanjili smo polovicu veli\u010dine. Ova tehnika mo\u017ee se koristiti zahvaljuju\u0107i na\u0161em perceptivnom percepciji boja. \u010covjek \u0107e lako primijetiti razliku u svjetlini, ali ne i u boji, ako je prosjek unutar malog bloka piksela. Tako\u0111er, prore\u0111ivanje se mo\u017ee vr\u0161iti u liniji, 4 piksela horizontalno i vertikalno. Prva opcija se \u010de\u0161\u0107e koristi. Ako je kvaliteta slike va\u017ena, prore\u0111ivanje se uop\u0107e ne vr\u0161i.
\nIlustracija smanjenja uzorka (Habr nije dopustio dodavanje gif-a) \u2014 https:\/\/i.ibb.co\/Rg5Th9H\/150953010617579181.gif<\/a><\/noindex><\/p>\n

<\/p>\n

Glavni dio pripreme<\/h5>\n

\nDCT<\/p>\n

Sada najte\u017ei i najpotrebniji dio. Cjela slika se dijeli na blokove 8\u00d78 (koristi se ispunjavanje u slu\u010daju da rezolucija nije vi\u0161ekratnik stranice bloka).<\/p>\n

<\/p>\n

Sada se svakom bloku primjenjuje DCT (Diskretna kosinusna transformacija)<\/strong>. U ovom dijelu slike uklanja se sve suvi\u0161no. Koriste\u0107i DCT treba razumjeti opisuje li ovaj blok (8\u00d78) neku monotonu dijela slike: neba, zida; ili sadr\u017ei slo\u017eenu strukturu (kosa, simboli itd.). Logi\u010dno je da se 64 sli\u010dnih po boji piksela mo\u017ee opisati s jednim, jer je veli\u010dina bloka ve\u0107 poznata. Eto vam kompresije: 64 prema 1.<\/p>\n

<\/p>\n

DCT pretvara blok u spektar, i gdje se o\u010ditanja o\u0161tro mijenjaju, koeficijent postaje pozitivan, a \u0161to je prijelaz o\u0161triji, to \u0107e izlaz biti ve\u0107i. Tamo gdje je koeficijent vi\u0161i, na slici su prikazani jasni prijelazi u boji i svjetlini, a gdje je ni\u017ei \u2014 slabi (glatki) prijelazi magnituda komponenti YCbCr u bloku.<\/p>\n

\nKvantizacija<\/p>\n

Ovdje se ve\u0107 primjenjuju postavke kompresije. Svaki od koeficijenata u svakoj od matrica 8\u00d78 dijeli se s odre\u0111enim brojem. Ako kvaliteta slike nakon svih njegovih modifikacija vi\u0161e ne \u017eelite smanjivati, onda bi djelitelj trebao biti jedan. Ako vam je va\u017enija memorija koju zauzima ova fotografija, onda \u0107e djelitelj biti ve\u0107i od 1, a koli\u010dnik se zaokru\u017euje. Tako se \u010desto doga\u0111a da nakon zaokru\u017eivanja ispada mnogo nula.<\/p>\n

<\/p>\n

Kvantizacija se radi kako bi se omogu\u0107ila jo\u0161 ve\u0107a kompresija. Evo kako to izgleda na primjeru kvantizacije grafa y = sin(x):<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

\nKompresija<\/p>\n

Prvo prolazimo kroz matricu u zig-zag putanji:<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

<\/p>\n

Dobijamo jednodimenzionalni niz s brojevima. Vidimo da ih ima puno nula, koje mo\u017eemo ukloniti. Za to umjesto niza s mnogo nula upisujemo 1 nulu, a nakon nje broj koji ozna\u010dava njihov broj u nizu. Na taj na\u010din mo\u017eemo smanjiti veli\u010dinu cijelog niza na 1\/3. A zatim jednostavno kompresiramo ovaj niz metodom Huffmana i upisujemo u sam datoteku.<\/p>\n

<\/p>\n

Gdje se koristi<\/h3>\n

<\/p>\n

Svugdje. Kao i PNG, JPEG se koristi u fotoaparatima, OS-ima (kao logotipi tvrtki, ikone aplikacija, sli\u010dice) i u svim mogu\u0107im podru\u010djima gdje je potrebno u\u010dinkovito pohranjivanje slika.<\/p>\n

<\/p>\n

Izvje\u0161taj<\/h3>\n

<\/p>\n

Trenutno su znanja o JPEG-u vrijedna isklju\u010divo u obrazovne svrhe, jer je ve\u0107 svuda ugra\u0111en i optimiziran od strane velikih grupa ljudi, ali znanstveni kamen je ipak slastan.<\/p>\n

\nIzvori<\/p>\n

\u010clanak o YCbCr na Wikipediji<\/a><\/noindex>
\n\u010clanak o JPEG-u na Wikipediji<\/a><\/noindex>
\nMalo o DCT-u sa Pikabu posta<\/a><\/noindex>
\n\u010clanak o DCT-u na Wikipediji<\/a><\/noindex><\/p>\n

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

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