{"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\/ro\/blog\/administrirovanie\/jpeg-algoritm-szhatiya","title":{"rendered":"JPEG. Algoritm de compresie","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Bun\u0103 din nou! Am g\u0103sit acest articol, scris \u00eenc\u0103 \u00een mai 2019. Aceasta este o continuare a unei serii de articole despre WAVE \u0219i JPEG, iat\u0103 prima<\/a><\/noindex>. Aceast\u0103 publica\u021bie va con\u021bine informa\u021bii despre algoritmul de codare a imaginilor \u0219i despre formatul \u00een sine.<\/p>\n

<\/p>\n

O mig\u0103 de istorie<\/h3>\n

<\/p>\n

O lingur\u0103 de articol din Wikipedia:<\/p>\n

<\/p>\n

JPEG (Joint Photographic Experts Group) este unul dintre cele mai populare formate de grafic\u0103 raster, folosit pentru stocarea imaginilor foto \u0219i a imaginilor asem\u0103n\u0103toare.<\/p><\/blockquote>\n

Acest standard a fost dezvoltat de c\u0103tre grupul comun de exper\u021bi \u00een fotografie \u00eenc\u0103 din 1991 pentru comprimarea eficient\u0103 a imaginilor.<\/p>\n

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

Ce cale parcurg imaginile de la forma brut\u0103 la JPEG<\/h3>\n

<\/p>\n

Unii consider\u0103 c\u0103 imaginile JPEG sunt date brute comprimate folosind metoda Huffman, dar nu este a\u0219a. \u00cenainte de comprimarea final\u0103, datele trec printr-un drum lung.<\/p>\n

<\/p>\n

Mai \u00eent\u00e2i, modelul de culoare este schimbat din RGB \u00een YCbCr. Pentru aceasta exist\u0103 un algoritm special \u2014 aici<\/a><\/noindex>. Y nu este atins, deoarece este responsabil pentru luminozitate, iar modificarea sa va fi vizibil\u0103.<\/p>\n

<\/p>\n

Primul lucru care se face cu imaginea este \u201esub\u021biere\u201c<\/strong> (subsampling). E simplu de \u00een\u021beles: se ia un bloc de 2x2 pixeli, apoi se ob\u021bin Cb \u0219i Cr \u2013 valorile medii ale fiec\u0103rui component YCbCr din ace\u0219ti 4 pixeli. Astfel, am c\u00e2\u0219tigat 6 octe\u021bi<\/em>, \u00een loc de 4 Y, 4 Cb, 4 Cr, am ob\u021binut 4 Y \u0219i acelea\u0219i Cb \u0219i Cr pentru fiecare dintre ele (4 + 4 + 4 = 12; 4 + 1 + 1 = 6; 12 \u2014 6 = 6). La scara chiar de 2\u00d72, compresia cu pierderi cu un coeficient de compresie 2:1 sun\u0103 solid. Aceasta se aplic\u0103 \u00eentregului imagini. Astfel, am redus dimensiunea cu jum\u0103tate. Aceast\u0103 tehnic\u0103 poate fi utilizat\u0103 datorit\u0103 percep\u021biei noastre de culoare. O persoan\u0103 va observa cu u\u0219urin\u021b\u0103 diferen\u021ba de str\u0103lucire, dar nu \u0219i de culoare, dac\u0103 aceasta este mediat\u0103 \u00eentr-un mic bloc de pixeli. De asemenea, sub\u021bierea poate fi efectuat\u0103 \u00een linie, 4 pixeli pe orizontal\u0103 \u0219i vertical\u0103. Prima variant\u0103 este folosit\u0103 mai frecvent. Dac\u0103 calitatea imaginii este important\u0103, sub\u021bierea nu se efectueaz\u0103 deloc.
\nO ilustra\u021bie vizual\u0103 a subsamplingului (Habr nu a permis inserarea unui gif) \u2014 https:\/\/i.ibb.co\/Rg5Th9H\/150953010617579181.gif<\/a><\/noindex><\/p>\n

<\/p>\n

Partea principal\u0103 a preg\u0103tirii<\/h5>\n

\nDCT<\/p>\n

Acum, cea mai dificil\u0103 \u0219i necesar\u0103 parte. \u00centreaga imagine este \u00eemp\u0103r\u021bit\u0103 \u00een blocuri de 8\u00d78 (se folose\u0219te umplerea \u00een cazul \u00een care rezolu\u021bia nu este multipl\u0103 cu latura blocului).<\/p>\n

<\/p>\n

Acum se aplic\u0103 DCT (Transformata Cosinus Discret\u0103) la fiecare bloc<\/strong>. \u00cen aceast\u0103 parte, din imagine se scot toate informa\u021biile inutile. Folosind DCT, trebuie s\u0103 \u00een\u021belegem dac\u0103 acest bloc (8\u00d78) descrie o parte monoton\u0103 a imaginii: cer, perete; sau con\u021bine o structur\u0103 complex\u0103 (p\u0103r, simboluri etc.). Logic, 64 de pixeli similari ca culoare pot fi descri\u0219i cu unul singur, deoarece dimensiunea blocului este deja cunoscut\u0103. Iat\u0103 compresia: 64 la 1.<\/p>\n

<\/p>\n

DCT transform\u0103 blocul \u00eentr-un spectru, iar acolo unde citirile se schimb\u0103 brusc, coeficientul devine pozitiv, iar cu c\u00e2t tranzi\u021bia este mai abrupt\u0103, cu at\u00e2t mai mare va fi ie\u0219irea. Acolo unde coeficientul este mai mare, pe imagine sunt reprezentate tranzi\u021bii clare de culoare \u0219i luminozitate, iar unde este mai mic \u2013 schimb\u0103ri slabe (lineare) ale valorilor componentelor YCbCr din bloc.<\/p>\n

\nCuantificare<\/p>\n

Aici se aplic\u0103 deja set\u0103rile de compresie. Fiecare coeficient din fiecare dintre matricele 8\u00d78 este \u00eemp\u0103r\u021bit la un num\u0103r specific. Dac\u0103 calitatea imaginii dup\u0103 toate modific\u0103rile nu mai dore\u0219ti s\u0103 o reduci, atunci divizorul trebuie s\u0103 fie unul. Dac\u0103 mai important\u0103 este memoriile ocupate de aceast\u0103 fotografie, atunci divizorul va fi mai mare dec\u00e2t 1, iar c\u00e2tul este rotunjit. Astfel, se dovede\u0219te c\u0103, dup\u0103 rotunjire, adesea apar multe zerouri.<\/p>\n

<\/p>\n

Cuantificarea se face pentru a permite o comprimare \u0219i mai mare. Iat\u0103 cum arat\u0103 pe exemplul cuantific\u0103rii graficului y = sin(x):<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

\nCompresie<\/p>\n

Mai \u00eent\u00e2i, parcurgem matricea \u00een zig-zag:<\/p>\n

<\/p>\n

\"JPEG.<\/p>\n

<\/p>\n

Ob\u021binem un array unidimensional cu numere. Vedem c\u0103 exist\u0103 multe zerouri \u00een el, care pot fi eliminate. Pentru aceasta, \u00een locul unei secven\u021be cu multiple zerouri, scriem un singur zero \u0219i dup\u0103 acesta un num\u0103r care reprezint\u0103 cantitatea lor \u00een secven\u021b\u0103. Astfel, se poate reduce dimensiunea total\u0103 a array-ului la 1\/3. Apoi, se comprim\u0103 acest array folosind metoda Huffman \u0219i se scrie deja \u00een fi\u0219ierul propriu.<\/p>\n

<\/p>\n

Unde se folose\u0219te<\/h3>\n

<\/p>\n

Pretutindeni. La fel ca PNG, JPEG este folosit \u00een camere foto, sisteme de operare (ca logo-uri pentru companii, icoane pentru aplica\u021bii, miniaturi) \u0219i \u00een toate domeniile posibile unde este necesar\u0103 stocarea eficient\u0103 a imaginilor.<\/p>\n

<\/p>\n

Ie\u0219ire<\/h3>\n

<\/p>\n

\u00cen prezent, cuno\u0219tin\u021bele despre JPEG sunt valoroase doar \u00een scopuri educa\u021bionale, deoarece este deja incorporat peste tot \u0219i optimizat de echipe mari de oameni, dar granitul \u0219tiin\u021bei este totu\u0219i gustos.<\/p>\n

\nSurse<\/p>\n

Articol despre YCbCr pe Wikipedia<\/a><\/noindex>
\nArticol despre JPEG pe Wikipedia<\/a><\/noindex>
\nUn pic despre DCT dintr-o postare de Pechabu<\/a><\/noindex>
\nArticol despre DCT pe Wikipedia<\/a><\/noindex><\/p>\n

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

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