{"id":41842,"date":"2020-02-17T05:33:02","date_gmt":"2020-02-17T02:33:02","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/novyj-proekt-dlya-udaleniya-artefaktov-iz-jpeg"},"modified":"2020-02-17T05:33:02","modified_gmt":"2020-02-17T02:33:02","slug":"novyj-proekt-dlya-udaleniya-artefaktov-iz-jpeg","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/novyj-proekt-dlya-udaleniya-artefaktov-iz-jpeg","title":{"rendered":"Novo projeto para remover artefatos de JPEG","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

O projeto foi publicado no GitHub JPEG Quant Smooth<\/a><\/noindex>, destinado a remover artefatos de compress\u00e3o de imagens JPEG. Isso \u00e9 alcan\u00e7ado por meio da recupera\u00e7\u00e3o da precis\u00e3o dos coeficientes da transformada discreta do cosseno. Mas dentro dos limites permitidos pela tabela de quantiza\u00e7\u00e3o, isso n\u00e3o permite que o algoritmo re-filtre a imagem. O c\u00f3digo \u00e9 escrito em C com otimiza\u00e7\u00f5es para SSE2, AVX2 e ARM Neon. Tamb\u00e9m est\u00e1 dispon\u00edvel<\/a><\/noindex> existe uma vers\u00e3o em WebAssembly, que permite processar imagens no navegador (mas mais lenta que a vers\u00e3o nativa devido \u00e0 falta de otimiza\u00e7\u00f5es SIMD e multithreading).<\/p>\n

Segundo o autor, seu algoritmo funciona significativamente mais r\u00e1pido que um projeto semelhante jpeg2png<\/a><\/noindex> e produz imagens mais n\u00edtidas. Mas o autor admite que o jpeg2png pode ter um desempenho melhor em casos complexos. Outro projeto, knusperli<\/a><\/noindex>, publicado sob uma conta do Google — que tamb\u00e9m deve remover artefatos JPEG, segundo o autor «n\u00e3o serve para nada» e «\u00e9 dif\u00edcil perceber melhorias no seu trabalho».<\/p>\n

\"Novo<\/a><\/noindex>\"Novo<\/a><\/noindex><\/center><\/p>\n

\"Novo<\/a><\/noindex>\"Novo<\/a><\/noindex><\/center><\/p>\n

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

Fonte: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041d\u0430 GitHub \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d \u043f\u0440\u043e\u0435\u043a\u0442 JPEG Quant Smooth, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044b\u0439 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0430\u0440\u0442\u0435\u0444\u0430\u043a\u0442\u043e\u0432 \u0441\u0436\u0430\u0442\u0438\u044f \u0438\u0437 JPEG-\u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u042d\u0442\u043e \u0434\u043e\u0441\u0442\u0438\u0433\u0430\u0435\u0442\u0441\u044f \u0437\u0430 \u0441\u0447\u0451\u0442 \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432 \u0434\u0438\u0441\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u043a\u043e\u0441\u0438\u043d\u0443\u0441 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f. \u041d\u043e \u0432 \u0442\u0435\u0445 \u043f\u0440\u0435\u0434\u0435\u043b\u0430\u0445, \u0447\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0442\u0430\u0431\u043b\u0438\u0446\u0430 \u043a\u0432\u0430\u043d\u0442\u0438\u0437\u0430\u0446\u0438\u0438, \u044d\u0442\u043e \u043d\u0435 \u0434\u0430\u0451\u0442 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0443 \u043f\u0435\u0440\u0435\u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432\u0430\u0442\u044c \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u0421\u0438 \u0441 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f\u043c\u0438 \u0434\u043b\u044f SSE2, AVX2 \u0438 ARM Neon. \u0422\u0430\u043a\u0436\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430 WebAssembly-\u0432\u0435\u0440\u0441\u0438\u044f, 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