Scalar and Vector Quantization

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Alternative MSQE Derivation We can also compute the “power” of quantization error q = x – Q(x), q ∈ [–Δ/2, Δ/2] by: σ q 2 1 / 2 2 2 ∆ = ∫ ∆ q dq = ∆ −∆ / 2 12 . 14/55
The SNR of <strong>Quantization</strong> For n-bit uniform quantization of an uniform source of [–X max , X max ], the SNR is 6.02n dB, where n = log 2 M: 10log 10 ⎛ 2 ⎜ σ s 2 ⎝ σ q ⎞ ⎟ ⎠ = 10log 10 ⎛ (2X ⎜ ⎝ 12 max ) 2 12 ⋅ 2 ∆ ⎞ ⎟ ⎠ = 10log = 20log 10 10 ⎛ ⎜ ⎜ (2X ⎜ 12 ⎜ ⎝ 2 n max ) 2 ⎛ ⎜ ⎝ 12 2X M = 6.02n dB. max ⎞ ⎟ ⎠ 2 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ = 10log 10 M 2 15/55
Page 1 and 2: Scalar and Vector Quantization Nati
Page 3 and 4: The Quantization Problem Encoder m
Page 5 and 6: Quantization Function A quantizer
Page 7 and 8: Quantization Error If the quantiza
Page 9 and 10: Quantized Bitrate with VLC For VLC
Page 11 and 12: Uniform Quantizer All intervals ar
Page 13: Uniform Quantization of Uniform Sou
Page 17 and 18: Quantization of Non-uniform Sources
Page 19 and 20: Solving for Optimum Step Sizes Giv
Page 21 and 22: Variance Mismatch Effects (1/2) Ef
Page 23 and 24: Distribution Mismatch Effects Give
Page 25 and 26: Forward Adaptive Quantization (FAQ)
Page 27 and 28: Speech Quantization Example (2/2)
Page 29 and 30: Backward Adaptive Quantization (BAQ
Page 31 and 32: Output Levels of 3-bit Jayant Quant
Page 33 and 34: Picking Jayant Multipliers We must
Page 35 and 36: Jayant Quantizer Performance To av
Page 37 and 38: pdf-optimized Quantization Given f
Page 39 and 40: Lloyd-Max Algorithm (2/3) Begin wi
Page 41 and 42: Example: pdf-Optimized Quantizers
Page 43 and 44: Companded Quantization (CQ) In com
Page 45 and 46: Example: CQ (2/2) The expandar fun
Page 47 and 48: Entropy Coding of Quantizer Outputs
Page 49 and 50: Why Vector Quantization (1/2)? Cor
Page 51 and 52: Vector Quantization Rule Vector qu
Page 53 and 54: The Linde-Buzo-Gray Algorithm 1. St
Page 55: Impact of Training Set The trainin

Scalar and Vector Quantization

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