Hybrid No-Reference Video Quality Models for H.264 with Encrypted Payload

C. Lee, G. Seo, H. Choi, S. Youn, K. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a hybrid no-reference video quality measurement method for H.264. Although there is a strong need from the industry for no-reference models that don't require reference signals, developing reliable no-reference models for perceptual video quality assessment has been a challenging goal. On the other hand, in most digital video transmission, encoded video signals are transmitted in video packets, which can provide valuable information about coding parameters and transmission errors. The proposed hybrid noreference models uses both the information available from video packets and decoded signals to estimate coding artefacts and impairments caused by transmission errors. Sometime, the payloads of video packets may be encrypted to protect contents. In such case, the available information is rather limited. The proposed method was independently tested against large databases and shows good performance.

Original languageEnglish
Title of host publication11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738123462
DOIs
Publication statusPublished - 2020 Jul 15
Event11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020 - Piraeus, Greece
Duration: 2020 Jul 152020 Jul 17

Publication series

Name11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020

Conference

Conference11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
CountryGreece
CityPiraeus
Period20/7/1520/7/17

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program, 10035389, funded by the Ministry of Knowledge Economy (MKE, Korea).

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization

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