We present in this chapter the advantage of applying machine-learning-based perceptual coding strategies in relieving bandwidth limitation for wireless multimedia communications. Typical video-coding standards, especially the state-of-the-art high efficiency video coding (HEVC) standard as well as recent research progress on perceptual video coding, are included in this chapter. We further demonstrate an example that minimizes the overall perceptual distortion by modeling subjective quality with machine-learning-based saliency detection. We also present several promising directions in learning-based perceptual video coding to further enhance wireless multimedia communication experience.

Chapter Contents:

• 8.1 Background
• 8.2 Literature review on perceptual video coding
• 8.2.1 Perceptual models
• 8.2.1.1 Manual identification
• 8.2.1.2 Automatic identification
• 8.2.2 Incorporation in video coding
• 8.2.2.1 Model-based approaches
• 8.2.2.2 Learning-based approaches
• 8.3 Minimizing perceptual distortion with the RTE method
• 8.3.1 Rate control implementation on HEVC-MSP
• 8.3.2 Optimization formulation on perceptual distortion
• 8.3.3 RTE method for solving the optimization formulation
• 8.3.4 Bit reallocation for maintaining optimization
• 8.4 Computational complexity analysis
• 8.4.1 Theoretical analysis
• 8.4.2 Numerical analysis
• 8.5 Experimental results on single image coding
• 8.5.1 Test and parameter settings
• 8.5.2 Assessment on rate–distortion performance
• 8.5.3 Assessment of BD-rate savings
• 8.5.4 Assessment of control accuracy
• 8.5.5 Generalization test
• 8.6 Experimental results on video coding
• 8.6.1 Experiment
• 8.6.1.1 Settings
• 8.6.1.2 Evaluation on R–D performance
• 8.6.1.3 Evaluation on RC accuracy
• 8.7 Conclusion
• References

Inspec keywords: video coding; multimedia communication; learning (artificial intelligence)

Other keywords: perceptual distortion; wireless multimedia communication experience; typical video-coding standards; machine-learning-based saliency detection; wireless multimedia communications; machine-learning-based perceptual video coding; chapter; state-of-the-art high efficiency video coding standard; machine-learning-based perceptual coding strategies

Subjects: Codes; Optimisation techniques; Optical, image and video signal processing; Computer vision and image processing techniques; Optimisation techniques; Knowledge engineering techniques; Image and video coding; Multimedia communications; Video signal processing