On the impact of the GOP size in a temporal H.264/AVC-to-SVC transcoder in baseline and main profile

Garrido-Cantos, Rosario; Cock, Jan De; Martínez, José Luis; Leuven, Sebastiaan Van; Cuenca, Pedro; Garrido, A.; Walle, Rik Van de

doi:10.1007/s00530-012-0287-8

Cited by 2 publications

(2 citation statements)

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“…For the results of the proposal, encoded bitstreams in H.264/AVC were transcoded using the technique described in the previous section and different GOP lengths (4, 8 and 16) were used. Since most of the SVC encoding time is spent on the temporal enhancement layers with the two highest identifiers, as shown in [6], our approach will be applied to these temporal layers and the remaining temporal layers will be decoded and re-encoded completely. If there is only one temporal enhancement layer, it will only be applied to this one to avoid changes in the base temporal layer.…”

Section: Scenario and Metricsmentioning

confidence: 99%

H.264/AVC-to-SVC temporal video transcoder for video broadcasting in wireless networks

Garrido-Cantos

Cock

Martínez

et al. 2014

Multimed Tools Appl

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Over the last few decades multimedia usage has changed dramatically, with networks and terminals of diverse bandwidths and capabilities coexisting. To ensure a good quality of experience, this diverse environment requires adaptability of the video stream. If video streams were compressed using Scalable Video Coding (SVC) schemes, they would be able to adapt to these heterogeneous networks and a wide range of terminals. However, since the majority of multimedia contents are compressed using H.264/AVC, they cannot benefit from this scalability. This paper makes some proposals for improving a temporal H.264/AVC-to-SVC video transcoder so that it can convert an H.264/AVC bitstream without scalability to scalable bitstreams with temporal scalability. The idea behind the proposed transcoder is to accelerate interprediction, focusing on reducing the coding complexity of the mode decision and motion estimation tasks of the encoder stage by using information available after the H.264/AVC decoding stage. To accelerate the mode decision task, a decision tree for narrowing down the Macroblock (MB) types to be checked by the SVC encoder has been developed. On the other hand, to accelerate the motion estimation task, a reduced area is created dynamically for every MB and sub-MB. Moreover, time savings increase considerably when both proposals are adjusted to work together, as opposed to doing so in an isolated way. The results show that when our joint proposal is applied, the complexity is reduced by 98 % while maintaining coding efficiency

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Section: Scenario and Metricsmentioning

confidence: 99%

H.264/AVC-to-SVC temporal video transcoder for video broadcasting in wireless networks

Garrido-Cantos

Cock

Martínez

et al. 2014

Multimed Tools Appl

Self Cite

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“…Additionally, availability to download for public use makes them appropriate candidates to be used in a scientific research environment. In these experiments, the catalogue has been transcoded into 5 different resolutions -180p, 360p, 480p, 720p, 1080p with h264 encoding using libx264 with main profile using adaptive bitrate [14]. All these industry…”

Section: Video Cataloguementioning

confidence: 99%

Predicting quality of experience for online video service provisioning

Bulkan

Dagiuklas

2019

Multimed Tools Appl

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The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics.

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On the impact of the GOP size in a temporal H.264/AVC-to-SVC transcoder in baseline and main profile

Cited by 2 publications

References 15 publications

H.264/AVC-to-SVC temporal video transcoder for video broadcasting in wireless networks

H.264/AVC-to-SVC temporal video transcoder for video broadcasting in wireless networks

Predicting quality of experience for online video service provisioning

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