{"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","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Auf GitHub wurde ein Projekt ver\u00f6ffentlicht JPEG Quant Smooth<\/a><\/noindex>, das zur Entfernung von Kompressionsartefakten aus JPEG-Bildern dient. Dies wird durch die Wiederherstellung der Genauigkeit der diskreten Kosinustransformkoeffizienten erreicht. Innerhalb der Grenzen, die die Quantisierungstabelle zul\u00e4sst, kann der Algorithmus das Bild jedoch nicht erneut filtern. Der Code ist in C geschrieben und optimiert f\u00fcr SSE2, AVX2 und ARM Neon. Au\u00dferdem ist verf\u00fcgbar<\/a><\/noindex> gibt es eine WebAssembly-Version, die eine Bildverarbeitung im Browser erm\u00f6glicht (aber langsamer als die native Version aufgrund des Fehlens von SIMD-Optimierungen und Multithreading).<\/p>\n

Laut dem Autor arbeitet sein Algorithmus deutlich schneller als das \u00e4hnliche Projekt jpeg2png<\/a><\/noindex> und liefert klarere Bilder. Aber der Autor erkennt an, dass jpeg2png in komplizierten F\u00e4llen besser abschneiden kann. Ein weiteres Projekt, knusperli<\/a><\/noindex>, ver\u00f6ffentlicht unter dem Google-Konto — das auch JPEG-Artefakte entfernen soll, angeblich \"nicht geeignet f\u00fcr irgendetwas\" und \"schwer zu bemerkende Verbesserungen seiner Arbeit\".<\/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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