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Content adaptive pre-filtering for video compression

  • Mehdi SaeediEmail author
  • Boris Ivanovic
  • Tomasz Stolarczyk
  • Ihab Amer
  • Gabor Sines
Original Paper
  • 14 Downloads

Abstract

Bitrate reduction with little to no degradation in visual perception is a long-standing challenge in video coding. This paper targets this challenge by adaptively filtering the content prior to video compression and in the preprocessing stage. This is done by applying a bilateral filter where the filter parameters are selected according to regional content complexity and estimated visual importance besides bitrate and quality requirements. A multi-scale metric based on 2D gradient is employed to determine bandwidth requirements of different regions. A random forest regression model is trained to predict distortion and bit requirements for a block, if it is filtered and encoded at a given quality. The predicted distortion and bit requirements are used to select filter parameters considering a cost function. The proposed approach is applied to both H.264 and HEVC encoders, with different GOP structures. The results show up to 60% bitrate reduction in terms of BD-Rate (about 20% on average) for the attempted test cases with little to no noticeable quality degradation.

Keywords

Adaptive Pre-filtering Machine learning Video encoding Visual perception 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.Advanced Micro Devices, Inc. (AMD)MarkhamCanada

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