Temporal capsule networks for video motion estimation and error concealment

Sankisa, Arun; Punjabi, Arjun; Katsaggelos, Aggelos K.

doi:10.1007/s11760-020-01671-x

Cited by 4 publications

(1 citation statement)

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“…The model is non-blind and requires knowledge of the location of the packet loss and the macroblocks above and below. The use of temporal capsule networks to encode video-related attributes is examined in [ 83 ]. It operates on three co-located “patches”, which are

pixel regions extracted from a video sequence.…”

Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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pixel regions extracted from a video sequence.…”

Section: Learning-based Transmissionmentioning

confidence: 99%