Recommendation ITU-R BS.1387-2
Policy on Intellectual Property Right (IPR)
1 Introduction
2 Applications
3 Versions
4 The subjective domain
5 Resolution and accuracy
6 Requirements and limitations
1 General
2 Main applications
2.1 Assessment of implementations
2.2 Perceptual quality line up
2.3 On-line monitoring
2.4 Equipment or connection status
2.5 Codec identification
2.6 Codec development
2.7 Network planning
2.8 Aid to subjective assessment
2.9 Summary of applications
3 Test signals
3.1 Selection of natural test signals
3.2 Duration
4 Synchronization
5 Copyright issues
1 Introduction
2 Model Output Variables
3 Basic Audio Quality
4 Coding Margin
5 User requirements
1 Audio processing
1.1 User-defined settings
1.2 Psycho-acoustic model
1.3 Cognitive model
1 Introduction and history
2 General structure of objective perceptual audio quality measurement
methods
3 Psycho-acoustical and cognitive basics
3.1 Outer and middle ear transfer characteristic
3.2 Perceptual frequency scales
3.3 Excitation
3.4 Detection
3.5 Masking
3.6 Loudness and partial masking
3.7 Sharpness
3.8 Cognitive processing
Example 1: Separation of linear from non-linear distortions
Example 2: Auditory scene analysis
Example 3: Informational masking
Example 4: Spectral-temporal weighting
4 Models incorporated
4.1 DIX
4.2 NMR
4.3 OASE
4.4 Perceptual Audio Quality Measure (PAQM)
4.5 PERCEVAL
4.6 POM
4.7 The toolbox approach
1 Outline
1.1 Basic Version
1.2 Advanced Version
2 Peripheral ear model
2.1 FFT-based ear model
2.1.1 Overview
2.1.2 Time processing
2.1.3 FFT
2.1.4 Outer and middle ear
2.1.5 Grouping into critical bands
2.1.6 Adding internal noise
2.1.7 Spreading
2.1.8 Time domain spreading
2.1.9 Masking threshold
2.2 Filter bank-based ear model
2.2.1 Overview
2.2.2 Subsampling
2.2.3 Setting of playback level
2.2.4 DC rejection filter
2.2.5 Filter bank
2.2.6 Outer and middle ear filtering
2.2.7 Frequency domain spreading
2.2.8 Rectification
2.2.9 Time domain smearing (1) – Backward
masking
2.2.10 Adding of internal noise
2.2.11 Time domain smearing (2) – Forward
masking
3 Pre-processing of excitation patterns
3.1 Level and pattern adaptation
3.1.1 Level adaptation
3.1.2 Pattern adaptation
3.2 Modulation
3.3 Loudness
3.4 Calculation of the error signal
4 Calculation of Model Output Variables
4.1 Overview
4.2 Modulation difference
4.2.1 RmsModDiffA
4.2.2 WinModDiff1B
4.2.3 AvgModDiff1B and AvgModDiff2B
4.3 Noise loudness
4.3.1 RmsNoiseLoudA
4.3.2 RmsMissingComponentsA
4.3.3 RmsNoiseLoudAsymA
4.3.4 AvgLinDistA
4.3.5 RmsNoiseLoudB
4.4 Bandwidth
4.4.1 Pseudocode
4.4.2 BandwidthRefB and BandwidthTestB
4.5 Noise-to-mask ratio
4.5.1 Total NMRB
4.5.2 Segmental NMRB
4.6 Relative Disturbed FramesB
4.7 Detection probability
4.7.1 Maximum Filtered Probability of Detection (MFPDB)
4.7.2 Average distorted block (ADBB)
4.8 Harmonic structure of error
4.8.1 EHSB
5 Averaging
5.1 Spectral averaging
5.1.1 Linear average
5.2 Temporal averaging
5.2.1 Linear average
5.2.2 Squared average
5.2.3 Windowed average
5.2.4 Frame selection
5.3 Averaging over audio channels
6 Estimation of the perceived basic audio quality
6.1 Artificial neural network
6.2 Basic Version
6.3 Advanced Version
7 Conformance of implementations
7.1 General
7.2 Selection
7.3 Settings for the conformance test
7.4 Acceptable tolerance interval
7.5 Test items
1 General
2 Competitive phase
3 Collaborative phase
4 Verification
4.1 Comparison of SDG and ODG values
4.2 Correlation
4.3 Absolute Error Score (AES)
4.4 Comparison of ODG versus the confidence interval
4.5 Comparison of ODG versus the tolerance-interval
5 Selection of the optimal model versions
5.1 Pre-selection criteria based on correlation
5.2 Analysis of number of outliers
5.3 Analysis of severeness of outliers
6 Conclusion
1 Introduction
2 Items per database
3 Experimental conditions
3.1 MPEG90
3.2 MPEG91
3.3 ITU92DI
3.4 ITU92CO
3.5 ITU93
3.6 MPEG95
3.7 EIA95
3.8 DB2
3.9 DB3
3.10 CRC97
4 Items per condition for DB2 and DB3
4.1 DB2
4.2 DB3