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Variance Adaptive Quantization and Adaptive Offset Selection in High Efficiency Video Coding
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Video compression uses encoding to reduce the number of bits that are used forrepresenting a video file in order to store and transmit it at a smaller size. Adecoder reconstructs the received data into a representation of the original video.Video coding standards determines how the video compression should beconducted and one of the latest standards is High Efficiency Video Coding (HEVC).One technique that can be used in the encoder is variance adaptive quantizationwhich improves the subjective quality in videos. The technique assigns lowerquantization parameter values to parts of the frame with low variance to increasequality, and vice versa. Another part of the encoder is the sample adaptive offsetfilter, which reduces pixel errors caused by the compression. In this project, thevariance adaptive quantization technique is implemented in the Ericsson researchHEVC encoder c65. Its functionality is verified by subjective evaluation. It isinvestigated if the sample adaptive offset can exploit the adjusted quantizationparameters values when reducing pixel errors to improve compression efficiency. Amodel for this purpose is developed and implemented in c65. Data indicates thatthe model can increase the error reduction in the sample adaptive offset. However,the difference in performance of the model compared to a reference encoder is notsignificant.

Place, publisher, year, edition, pages
2016. , 38 p.
UPTEC STS, ISSN 1650-8319 ; 16004
Keyword [en]
Video Coding, High Efficiency Video Coding, Sample Adaptive Offset filter, Adaptive Quantization Parameter
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-278155OAI: diva2:906171
External cooperation
Subject / course
Computer Systems Sciences
Educational program
Systems in Technology and Society Programme
Available from: 2016-02-24 Created: 2016-02-23 Last updated: 2016-02-24Bibliographically approved

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