Title

Video Quality And Traffic Qos In Learning-Based Subsampled And Receiver-Interpolated Video Sequences

Abstract

Neural networks are proposed as post-processors for any existing video compression scheme. The approach involves interpolating video sequences and compensating for frames which may have been lost or deliberately dropped. Deliberately dropping frames significantly reduces the amount of offered traffic in the network, the cell loss probability and network congestion, while the neural network post-processor will preserve most of the desired video quality. Dropping frames at the sender or in the network is also a fast way to react to network overload and reduce congestion. The interpolation techniques at the receiver provide output frame rates which are identical to the original video sequence's frame rate.

Publication Date

2-1-2000

Publication Title

IEEE Journal on Selected Areas in Communications

Volume

18

Issue

2

Number of Pages

150-167

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/49.824788

Socpus ID

0033871374 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033871374

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