Optimal bit allocation in the presence of quantizer feedback

The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous quantization. The solution to this problem has potential application to many forms of image and video coding where predictive coding is used...

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Published in:1993 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 5; pp. 385 - 388 vol.5
Main Authors: Uz, K.M., Shapiro, J.M., Czigler, M.
Format: Conference Proceeding
Language:English
Published: IEEE 1993
Subjects:
Bit rate
Feedback
Lagrangian functions
Nonlinear distortion
Nonlinear equations
Predictive coding
Predictive models
Quantization
Technological innovation
Video coding
ISBN:0780309464, 0780374029, 9780780309463, 9780780374027
ISSN:1520-6149
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Abstract The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous quantization. The solution to this problem has potential application to many forms of image and video coding where predictive coding is used. In predictive coding, the input to the quantizer can be decomposed into the innovation, i.e., the part of the quantizer input signal due to the quantization of the predictor. The natural question that arises is whether it is better to allocate more bits to the predictor, since quantization errors persist longer, or to allocate more bits to coding the total residual. This problem is analyzed for predictive video coding through the use of a simple parametric distortion-rate model for the propagation of quantization errors. This model provides a framework in which the optimal bit allocation problem can be solved in the presence of quantizer feedback. An exact MMSE (minimum mean-square error) solution is obtained that involves solving one nonlinear monotonic equation for one Lagrange multiplier, after which the bit allocation has a closed-form analytic solution. Since the MMSE solution does not produce equal distortion in all frames, the optimal MINMAX (minimize the maximum) bit allocation that minimizes the frame distortion subject to equal distortions per frame is also introduced.< >
AbstractList The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous quantization. The solution to this problem has potential application to many forms of image and video coding where predictive coding is used. In predictive coding, the input to the quantizer can be decomposed into the innovation, i.e., the part of the quantizer input signal due to the quantization of the predictor. The natural question that arises is whether it is better to allocate more bits to the predictor, since quantization errors persist longer, or to allocate more bits to coding the total residual. This problem is analyzed for predictive video coding through the use of a simple parametric distortion-rate model for the propagation of quantization errors. This model provides a framework in which the optimal bit allocation problem can be solved in the presence of quantizer feedback. An exact MMSE (minimum mean-square error) solution is obtained that involves solving one nonlinear monotonic equation for one Lagrange multiplier, after which the bit allocation has a closed-form analytic solution. Since the MMSE solution does not produce equal distortion in all frames, the optimal MINMAX (minimize the maximum) bit allocation that minimizes the frame distortion subject to equal distortions per frame is also introduced.< >
Author Uz, K.M.
Shapiro, J.M.
Czigler, M.
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  surname: Czigler
  fullname: Czigler, M.
  organization: David Sarnoff Res. Center, Princeton, NJ, USA
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Snippet The authors consider the problem of optimal bit allocation in various forms of predictive coding, where the predictor itself has errors resulting from previous...
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StartPage 385
SubjectTerms Bit rate
Feedback
Lagrangian functions
Nonlinear distortion
Nonlinear equations
Predictive coding
Predictive models
Quantization
Technological innovation
Video coding
Title Optimal bit allocation in the presence of quantizer feedback
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