Gop structure adaptive to the video content for efficient H.264/AVC encoding

Porto, Marcelo; Scharcanski, Jacob; Bampi, Sérgio

doi:10.1109/icip.2010.5651700

Cited by 31 publications

(22 citation statements)

References 2 publications

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“…A possible explanation for the variable GOP length is that the length is adaptive to the video content to reduce the video bit rate. Such an approach is for example proposed in [13]. Hence, the GOP structure for the ARD/ZDF life stream can be written as M = 1, N = variable.…”

Section: Assessed Gop Structuresmentioning

confidence: 99%

Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions

Klein

Zinner

Borchert

et al. 2013

Lecture Notes in Computer Science

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Abstract.A large fraction of the current Internet traffic is caused by video streaming. Due to the growing expectations of video consumers, monitoring video applications is getting more and more important for network and service providers. In a previous work, we proposed a video quality monitoring solution which utilizes the full reference SSIM metric to improve the monitoring in the network by distributing pre-computed distortion information induced by frame losses. To improve scalability, we introduced a less complex algorithm which infers the distortion for higher loss scenarios from single loss scenarios and inter-frame dependencies. In this work, we evaluate the accuracy of our algorithm by comparing it with the exact calculation of the SSIM metric for different frame loss scenarios. We further consider different high definition test video sequences and group of picture structures and investigate the influence on the accuracy of our proposed approximation.

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Section: Assessed Gop Structuresmentioning

confidence: 99%

Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions

Klein

Zinner

Borchert

et al. 2013

Lecture Notes in Computer Science

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“…In [9], an adaptive video coding control scheme is proposed that suggests using more P-and B-frame while the temporal correlation among the frames in a group of pictures (GOP) are high. A method for using different GOP size based on video content is presented in [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Reference Video Coding Using Stillness Detection

Chen¹,

Liu²,

Xu³

et al. 2018

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Encoders of AOM/AV1 codec consider an input video sequence as succession of frames grouped in Golden-Frame (GF) groups. The coding structure of a GF group is fixed with a given GF group size. In the current AOM/AV1 encoder, video frames are coded using a hierarchical, multilayer coding structure within one GF group. It has been observed that the use of multilayer coding structure may result in worse coding performance if the GF group presents consistent stillness across its frames. This paper proposes a new approach that adaptively designs the Golden-Frame (GF) group coding structure through the use of stillness detection. Our new approach hence develops an automatic stillness detection scheme using three metrics extracted from each GF group. It then differentiates those GF groups of stillness from other nonstill GF groups and uses different GF coding structures accordingly. Experimental result demonstrates a consistent coding gain using the new approach.

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“…This latter approach aims at the enhancement of video resiliency to channel errors during wireless transmissions. The works presented in [7] and [8] propose GoP structures adaptive to video content, without addressing resource allocation issues, at the same time.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation in visual sensor networks using a reinforcement learning framework

Pandremmenou

Tziortziotis

Kondi

et al. 2013

2013 18th International Conference on Digital Signal Processing (DSP)

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In recent years, video delivery over wireless visual sensor networks (VSNs) has g ained increasin g attention. The lossy compression and channel errors that occur durin g wire less multimedia transmissions can de g rade the quality of the transmitted video sequences. This paper addresses the problem of cross-layer resource allocation amon g the nodes of a wireless direct-sequence code division multiple access (DS-CDMA) VSN.The optimal g roup of pictures (GoP) len g th durin g the encodin g process is also considered, based on the motion level of each video sequence. Three optimization criteria that optimize a different objective function of the video qualities of the nodes are used. The nodes' transmission parameters, i.e., the source codin g rates, channel codin g rates and power levels can only take discrete values. In order to tackle the resultin g optimization problem, a reinforcement learning (RL) strate g y that promises efficient exploration and exploitation of the parameters' space is employed. This makes the proposed methodolo g y usable in lar g e or continuous state spaces as well as in an online mode.Experimental results hi g hli g ht the efficiency of the proposed method.

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Gop structure adaptive to the video content for efficient H.264/AVC encoding

Cited by 31 publications

References 2 publications

Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions

Evaluation of Video Quality Monitoring Based on Pre-computed Frame Distortions

Multi-Reference Video Coding Using Stillness Detection

Resource allocation in visual sensor networks using a reinforcement learning framework

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