Video quality in AVC homogenous transcoding

Abstract-In this paper the results obtained for homogenous Cascaded Pixel Domain Transcoder of AVC bitstreams are reported in order to show the expected transcoding efficiency gain/loss. A wide set of test video sequences has been used in experiments and in total 19200 bitstreams have been encoded and examined. It has been proved that there is a universal dependency between the quality, defined as PSNR and bitstream reduction. PSNR is described as the difference between quality of the transcoded material and the original material that could potentially be encoded at the same bitrate as the transcoded one

Robust multi-view video streaming through adaptive intra refresh video transcoding

A multi-view video (MVV) transcoder has been designed. The objective is to deliver maximum quality 3D video data from the source to the 2D video destination, through a wireless communication channel using all of its available bandwidth. This design makes use of the spatial and view downscaling algorithm. The method involves the reuse of motion information obtained from both the reference frames and views. Consequently, highly compressed MVV is converted into low bit rate single view video that is compliant with H.264/AVC format. Adaptive intra refresh (AIR) error resilience tool is configured to mitigate the error propagation resulting from channel conditions. Experimental results indicate that error resilience plus transcoding performed better than the cascaded technique. Simulation results demonstrated an efficient 3D video streaming service applied to low power mobile devices

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

Robust Multi-View Video Streaming through Adaptive Intra Refresh Video Transcoding

A multi-view video (MVV) transcoder has been designed. The objective is to deliver maximum quality 3D video data from the source to the 2D video destination, through a wireless communication channel using all of its available bandwidth. This design makes use of the spatial and view downscaling algorithm. The method involves the reuse of motion information obtained from both the reference frames and views. Consequently, highly compressed MVV is converted into low bit rate single view video that is compliant with H.264/AVC format. Adaptive intra refresh (AIR) error resilience tool is configured to mitigate the error propagation resulting from channel conditions. Experimental results indicate that error resilience plus transcoding performed better than the cascaded technique. Simulation results demonstrated an efficient 3D video streaming service applied to low power mobile devices

Interactive Video Coding

Projecte realitzat en el marc d’un programa de mobilitat amb la Linköpings Universitet[ANGLÈS] One of the main challenges when transcoding video is to find a proper balance between good quality and a manageable final size of the video file. A possible solution is to perform a trial and error procedure, transcoding the video several times until a good result is found. Besides being a tedious work it does not ensure finding an optimum solution. In this project a full system is developed to solve this problem. The solution is based on obtaining a complexity estimation of the video and to perform several short transcodifications at the most complex parts, testing different coding parameters. In addition, the user is able to handle all the process by selecting which parts to transcode and which parameters to test as many times as desired after watching the system results.[CASTELLÀ] En el proceso de transcodificación de videos, uno de los aspectos mas importantes a tener en cuenta es el compromiso entre buena calidad de video y tamaño reducido del fichero. Para encontrar una buena solución se puede intentar transcodificar el video varias veces hasta llegar a un resultado satisfactorio. Esto, a parte de ser un trabajo tedioso, no asegura la obtención de una solución óptima. En éste proyecto se ha desarrollado un sistema que soluciona el problema basándose en probar diferentes parámetros de codificación en las partes más complejas del video, tras hacer una estimación de complejidad. Además, el usuario es quién decide cuándo terminar el proceso a partir de los resultados obtenidos, pudiendo realizar tantas transcodificaciones como crea necesario y probando los parámetros de codificación que considere convenientes.[CATALÀ] A l’hora de transcodificar videos, una de les consideracions més importants és el compromís entre una bona qualitat de video i una mida de fitxer reduïda. Una possible solució és transcodificar el video diverses vegades fins a arribar a un bon resultat. Això, a banda de ser un treball molt feixuc, no assegura que s’obtingui un resultat òptim. En aquest projecte s’ha desenvolupat un sistema que soluciona el problema basant-se en trobar les parts més complexes d’un vídeo a partir d’una estimació de complexitat i aplicar allà diverses transcodificacions amb diferents paràmetres de codificació. A més, és l’usuari qui decideix com es desenvolupa el procés, podent demanar tantes transcodificacions com es cregui necessari per a provar els diferents paràmetres, després d’observar els resultats obtinguts

DAVID D2.2: Analysis of loss modes in preservation systems

This is a report on the way in which loss and damage to digital AV content occurs for different content types, AV data carriers and preservation systems.Three different loss modes have been identified, and each has been analysed in terms of existing solutions and longterm effects. This report also includes an in-depth treatment of format compatibility (interoperability issues), format resilience to carrier degradation and format resilience to corruption

Very low complexity mpeg-2 to h.264 transcoding using machine learning

ABSTRACT This paper presents a novel macroblock mode decision algorithm for inter-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast macro block (MB) mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming MPEG-2 MBs into one of the 11 coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. The proposed transcoder is compared with a reference transcoder comprised of a MPEG-2 decoder and an H.264 encoder. Our results show that the proposed transcoder reduces the H.264 encoding time by over 95% with negligible loss in quality and bitrate

Adaptive intra refresh for robust wireless multi-view video

This thesis was submitted for the award of PhD and was awarded by Brunel University LondonMobile wireless communication technology is a fast developing field and every day new mobile communication techniques and means are becoming available. In this thesis multi-view video (MVV) is also refers to as 3D video. Thus, the 3D video signals through wireless communication are shaping telecommunication industry and academia. However, wireless channels are prone to high level of bit and burst errors that largely deteriorate the quality of service (QoS). Noise along the wireless transmission path can introduce distortion or make a compressed bitstream lose vital information. The error caused by noise progressively spread to subsequent frames and among multiple views due to prediction. This error may compel the receiver to pause momentarily and wait for the subsequent INTRA picture to continue decoding. The pausing of video stream affects the user's Quality of Experience (QoE). Thus, an error resilience strategy is needed to protect the compressed bitstream against transmission errors. This thesis focuses on error resilience Adaptive Intra Refresh (AIR) technique. The AIR method is developed to make the compressed 3D video more robust to channel errors. The process involves periodic injection of Intra-coded macroblocks in a cyclic pattern using H.264/AVC standard. The algorithm takes into account individual features in each macroblock and the feedback information sent by the decoder about the channel condition in order to generate an MVV-AIR map. MVV-AIR map generation regulates the order of packets arrival and identifies the motion activities in each macroblock. Based on the level of motion activity contained in each macroblock, the MVV-AIR map classifies frames as high or low motion macroblocks. A proxy MVV-AIR transcoder is used to validate the efficiency of the generated MVV-AIR map. The MVV-AIR transcoding algorithm uses spatial and views downscaling scheme to convert from MVV to single view. Various experimental results indicate that the proposed error resilient MVV-AIR transcoder technique effectively improves the quality of reconstructed 3D video in wireless networks. A comparison of MVV-AIR transcoder algorithm with some traditional error resilience techniques demonstrates that MVV-AIR algorithm performs better in an error prone channel. Results of simulation revealed significant improvements in both objective and subjective qualities. No additional computational complexity emanates from the scheme while the QoS and QoE requirements are still fully met.Tertiary Institution Trust Fund (TETFund) of Nigeri

Toward a General Parametric Model for Assessing the Impact of Video Transcoding on Objective Video Quality

Video transcoding can cause degradation to an original video. Currently, there is no general model that assesses the impact of video transcoding on video quality. Such a model could play a critical role in evaluating the quality of the transcoded video, and thereby optimizing delivery of video to end-users while meeting their expectations. The main contribution of this research is the development and substantiation of a general parametric model, called the Video Transcoding Objective-quality Model (VTOM), that provides an extensible video transcoding service selection mechanism, which takes into account both the format and characteristics of the original video and the desired output, i.e., viewing format with preferred quality of service. VTOM represents a mathematical function that uses a set of media-related parameters for the original video and desired output, including codec, bit rate, frame rate, and frame size to predict the quality of the transcoded video generated from a specific transcoding. VTOM includes four quality sub-models, each describing the impact of each of these parameters on objective video quality, as well as a weighted-product aggregation function that combines these quality sub-models with four additional error sub-models in a single function for assessing the overall video quality. I compared the predicted quality results generated from the VTOM with quality values generated from an existing objective-quality metric. These comparisons yielded results that showed good correlations, with low error values. VTOM helps the researchers and developers of video delivery systems and applications to calculate the degradation that video transcoding can cause on the fly, rather than evaluate it statistically using statistical methods that only consider the desired output. Because VTOM takes into account the quality of the input video, i.e., video format and characteristics, and the desired quality of the output video, it can be used for dynamic video transcoding service selection and composition. A number of quality metrics were examined and used in development of VTOM and its assessment. However, this research discovered that, to date, there are no suitable metrics in the literature for comparing two videos with different frame rates. Therefore, this dissertation defines a new metric, called Frame Rate Metric (FRM) as part of its contributions. FRM can use any frame-based quality metric for comparing frames from both videos. Finally, this research presents and adapts four Quality of Service (QoS)-aware video transcoding service selection algorithms. The experimental results showed that these four algorithms achieved good results in terms of time complexity, success ratio, and user satisfaction rate