{"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\/es\/blog\/novyj-proekt-dlya-udaleniya-artefaktov-iz-jpeg","title":{"rendered":"Nuevo proyecto para eliminar artefactos de JPEG","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

El proyecto se ha publicado en GitHub JPEG Quant Smooth<\/a><\/noindex>, dise\u00f1ado para eliminar artefactos de compresi\u00f3n de im\u00e1genes JPEG. Esto se logra restaurando la precisi\u00f3n de los coeficientes de la transformada discreta del coseno. Sin embargo, dentro de los l\u00edmites que permite la tabla de cuantificaci\u00f3n, esto no permite que el algoritmo reprocesa la imagen. El c\u00f3digo est\u00e1 escrito en C con optimizaciones para SSE2, AVX2 y ARM Neon. Tambi\u00e9n est\u00e1 disponible<\/a><\/noindex> hay una versi\u00f3n en WebAssembly, que permite procesar im\u00e1genes en el navegador (aunque m\u00e1s lentamente que la versi\u00f3n nativa debido a la falta de optimizaciones SIMD y multihilo).<\/p>\n

Seg\u00fan el autor, su algoritmo funciona significativamente m\u00e1s r\u00e1pido que un proyecto similar jpeg2png<\/a><\/noindex> y produce im\u00e1genes m\u00e1s n\u00edtidas. Sin embargo, el autor reconoce que jpeg2png puede funcionar mejor en casos complejos. Otro proyecto, knusperli<\/a><\/noindex>, publicado bajo una cuenta de Google \u2014 que tambi\u00e9n deber\u00eda eliminar artefactos JPEG, seg\u00fan el autor \u00abno sirve para nada\u00bb y \u00abes dif\u00edcil notar mejoras de su trabajo\u00bb.<\/p>\n

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

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

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

Fuente: 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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