<p>In this thesis work a part of the video coding standard H.264 has been implemented. The part of the video coder that is used to code the I-frames has been implemented to see how well suited it is for regular image coding. The big difference versus other image coding standards, such as JPEG and JPEG2000, is that this video coder uses both a predictor and a transform to compress the I-frames, while JPEG and JPEG2000 only use a transform. Since the prediction error is sent instead of the actual pixel values, a lot of the values are zero or close to zero before the transformation and quantization. The method is much like a video encoder but the difference is that blocks of an image are predicted instead of frames in a video sequence.</p> / <p>I det här examensarbetet har en del av videokodningsstandarden H.264 implementerats. Den del av videokodaren som används för att koda s.k. I-bilder har implementerats för att testa hur bra den fungerar för ren stillbildskodning. Den stora skillnaden mot andra stillbildskodningsmetoder, såsom JPEG och JPEG2000, är att denna videokodaren använder både en prediktor och en transform för att komprimera stillbilderna, till skillnad från JPEG och JPEG2000 som bara använder en transform. Eftersom prediktionsfelen skickas istället för själva pixelvärdena så är många värden lika med noll eller nära noll redan innan transformationen och kvantiseringen. Metoden liknar alltså till mycket en ren videokodare, med skillnaden att man predikterar block i en bild istället för bilder i en videosekvens.</p>

<p>H.264, also known as MPEG-4 Part 10: Advanced Video Coding, is the latest MPEG standard for video coding. It provides approximately 50% bit rate savings for equivalent perceptual quality compared to any previous standard. In the same fashion as previous MPEG standards, only the bitstream syntax and the decoder are specified. Hence, coding performance is not only determined by the standard itself but also by the implementation of the encoder. In this report we propose two methods for improving the coding performance while remaining fully compliant to the standard. </p><p>After transformation and quantization, the transform coefficients are usually entropy coded and embedded in the bitstream. However, some of them might be beneficial to discard if the number of saved bits are sufficiently large. This is usually referred to as coefficient thresholding and is investigated in the scope of H.264 in this report. </p><p>Lagrangian optimization for video compression has proven to yield substantial improvements in perceived quality and the H.264 Reference Software has been designed around this concept. When performing Lagrangian optimization, lambda is a crucial parameter that determines the tradeoff between rate and distortion. We propose a new method to select lambda and the quantization parameter for non-reference frames in H.264. </p><p>The two methods are shown to achieve significant improvements. When combined, they reduce the bitrate around 12%, while preserving the video quality in terms of average PSNR. </p><p>To aid development of H.264, a software tool has been created to visualize the coding process and present statistics. This tool is capable of displaying information such as bit distribution, motion vectors, predicted pictures and motion compensated block sizes.</p>

<p>ITU - T and the Moving Picture Expert Group (MPEG) have jointly, under the name of Joint Video Team (JVT), developed a new video coding standard. The standard is called H.264 and is also known as Advanced Video Coding (AVC) or MPEG-4 part 10. Comparisons shows that H.264 greatly outperforms MPEG-2, currently used in DVD and digital TV. H.264 halves the bit rate with equal image quality. The great rate - distortion performance means nevertheless a high computational complexity. Especially on the encoder side.</p><p>Handling of audio and video, e.g. compressing and filtering, is quite complex and requires high performance hardware and software. A video encoder consists of a number of modules that find the best coding parameters. For each macroblock several $modes$ are evaluated in order to achieve optimal coding. The reference implementation of H.264 uses a brute force search for this mode selection which is extremely computational constraining. In order to perform video encoding with satisfactory speed there is an obvious need for reducing the amount of modes that are evaluated.</p><p>This thesis proposes an algorithm which reduces the number of modes and reference frames that are evaluated. The algorithm can be regulated in order to fulfill the demand on quality versus speed. Six times faster encoding can be obtained without loosing perceptual image quality. By allowing some quality degradation the encoding becomes up to 20 times faster.</p>

H.264, also known as MPEG-4 Part 10: Advanced Video Coding, is the latest MPEG standard for video coding. It provides approximately 50% bit rate savings for equivalent perceptual quality compared to any previous standard. In the same fashion as previous MPEG standards, only the bitstream syntax and the decoder are specified. Hence, coding performance is not only determined by the standard itself but also by the implementation of the encoder. In this report we propose two methods for improving the coding performance while remaining fully compliant to the standard. After transformation and quantization, the transform coefficients are usually entropy coded and embedded in the bitstream. However, some of them might be beneficial to discard if the number of saved bits are sufficiently large. This is usually referred to as coefficient thresholding and is investigated in the scope of H.264 in this report. Lagrangian optimization for video compression has proven to yield substantial improvements in perceived quality and the H.264 Reference Software has been designed around this concept. When performing Lagrangian optimization, lambda is a crucial parameter that determines the tradeoff between rate and distortion. We propose a new method to select lambda and the quantization parameter for non-reference frames in H.264. The two methods are shown to achieve significant improvements. When combined, they reduce the bitrate around 12%, while preserving the video quality in terms of average PSNR. To aid development of H.264, a software tool has been created to visualize the coding process and present statistics. This tool is capable of displaying information such as bit distribution, motion vectors, predicted pictures and motion compensated block sizes.

ITU - T and the Moving Picture Expert Group (MPEG) have jointly, under the name of Joint Video Team (JVT), developed a new video coding standard. The standard is called H.264 and is also known as Advanced Video Coding (AVC) or MPEG-4 part 10. Comparisons shows that H.264 greatly outperforms MPEG-2, currently used in DVD and digital TV. H.264 halves the bit rate with equal image quality. The great rate - distortion performance means nevertheless a high computational complexity. Especially on the encoder side. Handling of audio and video, e.g. compressing and filtering, is quite complex and requires high performance hardware and software. A video encoder consists of a number of modules that find the best coding parameters. For each macroblock several $modes$ are evaluated in order to achieve optimal coding. The reference implementation of H.264 uses a brute force search for this mode selection which is extremely computational constraining. In order to perform video encoding with satisfactory speed there is an obvious need for reducing the amount of modes that are evaluated. This thesis proposes an algorithm which reduces the number of modes and reference frames that are evaluated. The algorithm can be regulated in order to fulfill the demand on quality versus speed. Six times faster encoding can be obtained without loosing perceptual image quality. By allowing some quality degradation the encoding becomes up to 20 times faster.

In this thesis work a part of the video coding standard H.264 has been implemented. The part of the video coder that is used to code the I-frames has been implemented to see how well suited it is for regular image coding. The big difference versus other image coding standards, such as JPEG and JPEG2000, is that this video coder uses both a predictor and a transform to compress the I-frames, while JPEG and JPEG2000 only use a transform. Since the prediction error is sent instead of the actual pixel values, a lot of the values are zero or close to zero before the transformation and quantization. The method is much like a video encoder but the difference is that blocks of an image are predicted instead of frames in a video sequence. / I det här examensarbetet har en del av videokodningsstandarden H.264 implementerats. Den del av videokodaren som används för att koda s.k. I-bilder har implementerats för att testa hur bra den fungerar för ren stillbildskodning. Den stora skillnaden mot andra stillbildskodningsmetoder, såsom JPEG och JPEG2000, är att denna videokodaren använder både en prediktor och en transform för att komprimera stillbilderna, till skillnad från JPEG och JPEG2000 som bara använder en transform. Eftersom prediktionsfelen skickas istället för själva pixelvärdena så är många värden lika med noll eller nära noll redan innan transformationen och kvantiseringen. Metoden liknar alltså till mycket en ren videokodare, med skillnaden att man predikterar block i en bild istället för bilder i en videosekvens.

Disertační práce se zabývá metodami a prostředky pro hodnocení kvality obrazu ve videosekvencích, což je velmi aktuální téma, zažívající velký rozmach zejména v souvislosti s digitálním zpracováním videosignálů. Přestože již existuje relativně velké množství metod a metrik pro objektivní, tedy automatizované měření kvality videosekvencí, jsou tyto metody zpravidla založeny na porovnání zpracované (poškozené, například komprimací) a originální videosekvence. Metod pro hodnocení kvality videosekvení bez reference, tedy pouze na základě analýzy zpracovaného materiálu, je velmi málo. Navíc se takové metody převážně zaměřují na analýzu hodnot signálu (typicky jasu) v jednotlivých obrazových bodech dekódovaného signálu, což je jen těžko aplikovatelné pro moderní komprimační algoritmy jako je H.264/AVC, který používá sofistikovené techniky pro odstranění komprimačních artefaktů. V práci je nejprve podán stučný přehled dostupných metod pro objektivní hodnocení komprimovaných videosekvencí se zdůrazněním rozdílného principu metod využívajících referenční materiál a metod pracujících bez reference. Na základě analýzy možných přístupů pro hodnocení video sekvencí komprimovaných moderními komprimačními algoritmy je v dalším textu práce popsán návrh nové metody určené pro hodnocení kvality obrazu ve videosekvencích komprimovaných s využitím algoritmu H.264/AVC. Nová metoda je založena na sledování hodnot parametrů, které jsou obsaženy v transportním toku komprimovaného videa, a přímo souvisí s procesem kódování. Nejprve je provedena úvaha nad vlivem některých takových parametrů na kvalitu výsledného videa. Následně je navržen algoritmus, který s využitím umělé neuronové sítě určuje špičkový poměr signálu a šumu (peak signal-to-noise ratio -- PSNR) v komprimované videosekvenci -- plně referenční metrika je tedy nahrazována metrikou bez reference. Je ověřeno několik konfigurací umělých neuronových sítí od těch nejjednodušších až po třívrstvé dopředné sítě. Pro učení sítí a následnou analýzu jejich výkonnosti a věrnosti určení PSNR jsou vytvořeny dva soubory nekomprimovaných videosekvencí, které jsou následně komprimovány algoritmem H.264/AVC s proměnným nastavením kodéru. V závěrečné části práce je proveden rozbor chování nově navrženého algoritmu v případě, že se změní vlastnosti zpracovávaného videa (rozlišení, střih), případně kodéru (formát skupiny současně kódovaných snímků). Chování algoritmu je analyzováno až do plného vysokého rozlišení zdrojového signálu (full HD -1920 x 1080 obrazových bodů).

Le développement de réseaux de vidéosurveillance, civils ou militaires, pose des défis scientifiques et technologiques en termes d’analyse et de reconnaissance des contenus des flux compressés. Dans ce contexte, les contributions de cette thèse portent sur : - une méthode de segmentation automatique des objets mobiles (piétons, véhicules, animaux …) dans le domaine compressé, - la prise en compte des différents standards de compression les plus couramment utilisés en surveillance (MPEG-2, MPEG-4 Part 2 et MPEG-4 Part 10 / H.264 AVC), - une chaîne de traitement multi-flux optimisée depuis la segmentation des objets jusqu’à leur suivi et description. Le démonstrateur réalisé a permis d’évaluer les performances des approches méthodologiques développées dans le cadre d’un outil d’aide à l’investigation, identifiant les véhicules répondant à un signalement dans des bases de données de plusieurs dizaines d’heures. En outre, appliqué à des corpus représentatifs des différentes situations de vidéosurveillance (stations de métro, carrefours, surveillance de zones en milieu rural ou de frontières ...), le système a permis d’obtenir les résultats suivants : - analyse de 14 flux MPEG-2, 8 flux MPEG-4 Part 2 ou 3 flux AVC en temps réel sur un coeur à 2.66 GHZ (vidéo 720x576, 25 images par seconde), - taux de détection des véhicules de 100% sur la durée des séquences de surveillance de trafic, avec un taux de détection image par image proche des 95%, - segmentation de chaque objet sur 80 à 150% de sa surface (sous ou sur-segmentation liée au domaine compressé). Ces recherches ont fait l’objet du dépôt de 9 brevets liés à des nouveaux services et applications rendus opérationnels grâce aux approches mises en oeuvre. Citons entre autres des outils pour la protection inégale aux erreurs, la cryptographie visuelle, la vérification d’intégrité par tatouage ou l’enfouissement par stéganographie / The increasing deployment of civil and military videosurveillance networks brings both scientific and technological challenges regarding analysis and content recognition over compressed streams. In this context, the contributions of this thesis focus on: - an autonomous method to segment in the compressed domain mobile objects (pedestrians, vehicles, animals …), - the coverage of the various compression standards commonly used in surveillance (MPEG-2, MPEG-4 Part 2, MPEG-4 Part 10 / H.264 AVC), - an optimised multi-stream processing chain from the objects segmentation up to their tracking and description. The developed demonstrator made it possible to bench the performances of the methodological approaches chosen for a tool dedicated to help investigations. It identifies vehicles from a witness description in databases of tens of hours of video. Moreover, while dealing with corpus covering the different kind of content expected from surveillance (subway stations, crossroads, areas in countryside or border surveillance …), the system provided the following results: - simultaneous real time analysis of up to 14 MPEG-2 streams, 8 MPEG-4 Part 2 streams or 3 AVC streams on a single core (2.66 GHz; 720x576 video, 25 fps), - 100% vehicles detected over the length of traffic surveillance footages, with a image per image detection near 95%, - a segmentation spreading over 80 to 150% of the object area (under or over-segmentation linked with the compressed domain). These researches led to 9 patents linked with new services and applications that were made possible thanks to the suggested approaches. Among these lie tools for Unequal Error Protection, Visual Cryptography, Watermarking or Steganography