PVC-STIM: Perceptual video coding based on spatio-temporal influence map

Wang, Bing; Gao, Pan; Peng, Qiang; Zhang, Qianwen; Wu, Xiao; Wang, Xiang

doi:10.1007/s11760-022-02143-0

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Finally, many approaches have tried to exploit one of the many saliency models (or built their own) for the specific task of video compression [11], [27]- [30]. However, none of these methods directly collect the human spatio-temporal saliency maps explicitly for the task of video compression.…”

Section: B Saliency Map Collectionmentioning

confidence: 99%

An Interactive Annotation Tool for Perceptual Video Compression

Pergament¹,

Tandon²,

Tatwawadi³

et al. 2022

Preprint

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Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessment, human feedback is typically collected as a single scalar quality score indicating preference of one distorted video over another. In reality, some videos may be better in some parts but not in others. We propose an approach to collecting finergrained feedback by asking users to use an interactive tool to directly optimize for perceptual quality given a fixed bitrate. To this end, we built a novel web-tool which allows users to paint these spatio-temporal importance maps over videos. The tool allows for interactive successive refinement: we iteratively reencode the original video according to the painted importance maps, while maintaining the same bitrate, thus allowing the user to visually see the trade-off of assigning higher importance to one spatio-temporal part of the video at the cost of others. We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study -and are 1.9 times more likely to be preferred by viewers. The code for the tool and dataset can be found at https://github.com/jenyap/video-annotation-tool.git

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Section: B Saliency Map Collectionmentioning

confidence: 99%

An Interactive Annotation Tool for Perceptual Video Compression

Pergament¹,

Tandon²,

Tatwawadi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

An Interactive Annotation Tool for Perceptual Video Compression

Pergament

Tandon

Tatwawadi

et al. 2022

2022 14th International Conference on Quality of Multimedia Experience (QoMEX)

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Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments

Stawarz,

Ozog,

Łabuński

2024

Sensors

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Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors.

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PVC-STIM: Perceptual video coding based on spatio-temporal influence map

Cited by 3 publications

References 33 publications

An Interactive Annotation Tool for Perceptual Video Compression

An Interactive Annotation Tool for Perceptual Video Compression

An Interactive Annotation Tool for Perceptual Video Compression

Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments

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