Neural Network Guided Perceptually Optimized Bit-Allocation for Block-Based Image and Video Compression

Bit-allocation based on the MSE is computationally convenient in image and video compression, but leads to perceptually suboptimal compression results. Distortion sensitivity, modeled as a reference specific property, can be used to improve the accuracy of perceptual quality prediction based on the...

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Bibliographic Details
Main Authors: Bosse, Sebastian, Dietzel, Michael, Becker, Soren, Helmrich, Christian R., Siekmann, Mischa, Schwarz, Heiko, Marpe, Detlev, Wiegand, Thomas
Format: Conference Proceeding
Language:English
Subjects:
Distortion
Distortion measurement
distortion sensitivity
Image coding
Image compression
irrelevance reduction
neural network
perceptual coding
Quantization (signal)
Sensitivity
Video compression
Online Access:Request full text
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Summary:Bit-allocation based on the MSE is computationally convenient in image and video compression, but leads to perceptually suboptimal compression results. Distortion sensitivity, modeled as a reference specific property, can be used to improve the accuracy of perceptual quality prediction based on the MSE. This paper shows how distortion sensitivity directly leads to computationally beneficial perceptual optimization of irrelevance reduction and, thereby, of bit-allocation in image and video compression. To this end distortion sensitivity is estimated using a deep convolutional neural network. The proposed method of distortion sensitive bit-allocation is evaluated experimentally using HEVC and on our testset shows average bit-rate reductions with regard to the MOS of 15.9% compared to constant QP-based bit-allocation and 7.3% compared to state-of-the-art perceptual bit-allocation schemes.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8802925