{"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\/de\/blog\/novyj-proekt-dlya-udaleniya-artefaktov-iz-jpeg","title":{"rendered":"Neues Projekt zur Entfernung von Artefakten aus JPEG-Dateien.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Das Projekt wurde auf GitHub ver\u00f6ffentlicht JPEG Quant Smooth<\/a><\/noindex>, das zur Beseitigung von Kompressionsartefakten aus JPEG-Bildern entwickelt wurde. Dies geschieht durch die Wiederherstellung der Genauigkeit der Koeffizienten der diskreten Cosinustransformation. Innerhalb der Grenzen, die durch die Quantisierungstabelle festgelegt sind, erlaubt es dem Algorithmus jedoch nicht, das Bild neu zu filtern. Der Code ist in C geschrieben und optimiert f\u00fcr SSE2, AVX2 und ARM Neon. Au\u00dferdem verf\u00fcgbar<\/a><\/noindex> gibt es eine WebAssembly-Version, die es erm\u00f6glicht, Bilder im Browser zu verarbeiten (aber langsamer ist als die native Version aufgrund fehlender SIMD-Optimierungen und Multithreading).<\/p>\n

Laut dem Autor arbeitet sein Algorithmus deutlich schneller als ein \u00e4hnliches Projekt jpeg2png<\/a><\/noindex> und liefert klarere Bilder. Der Autor gesteht jedoch ein, dass jpeg2png in schwierigen F\u00e4llen besser funktionieren kann. Ein weiteres Projekt, knusperli<\/a><\/noindex>, ver\u00f6ffentlicht unter dem Google-Account \u2014 das ebenfalls JPEG-Artefakte entfernen soll, aber laut dem Autor \u201eunbrauchbar ist\u201c und \u201eschwer zu erkennende Verbesserungen\u201c aufweist.<\/p>\n

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

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

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

Quelle: 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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