System and method for modeling probability distribution functions of transform coefficients of encoded signal

• US 6,253,165 B1
• Filed: 06/30/1998
• Issued: 06/26/2001
• Est. Priority Date: 06/30/1998
• Status: Expired due to Term

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Malvar, Henrique S.
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): PHAN, THAI Q
• Application Number:
  US09/107,336
• Time in Patent Office: 1,092 Days
• Field of Search: 395/500.23, 704/258, 704/269, 704/268, 704/229, 704/230, 704/240, 704/255, 704/503, 703/2, 703/5
• US Class Current: 703/2
• CPC Class Codes: G10L 19/0212 using orthogonal transformation
• CPC Class Codes: H04S 2420/03 Application of parametric coding in stereophonic audio systems

Abstract

The coder/decoder (codec) system of the present invention includes a coder and a decoder. The coder includes a multi-resolution transform processor, such as a modulated lapped transform (MLT) transform processor, a weighting processor, a uniform quantizer, a masking threshold spectrum processor, an entropy encoder, and a communication device, such as a multiplexor (MUX) for multiplexing (combining) signals received from the above components for transmission over a single medium. The decoder comprises inverse components of the encoder, such as an inverse multi-resolution transform processor, an inverse weighting processor, an inverse uniform quantizer, an inverse masking threshold spectrum processor, an inverse entropy encoder, and an inverse MUX. With these components, the present invention is capable of performing resolution switching, spectral weighting, digital encoding, and parametric modeling.

Claims

1. In a system for processing audio signals having frequency-domain transform coefficients encoded by an encoder, a method for parametrically modeling source symbols of the encoder, comprising:

encoding incoming blocks of samples of the signal by the encoder to produce quantized coefficients;

computing a probability distribution function by building a mathematical transform and an exponential probability density function wherein the probability distribution function uses a combination of laplacian and exponential models that are fitted to incoming blocks of the samples; and

producing a dictionary of input strings from symbol probabilities based on the computed probability distribution function for use by the encoder to enhance processing efficiency of the signals.

2. The method of claim 1, wherein computing the probability distribution function is achieved with a single parameter determined from a maximum value of the quantized coefficients by utilizing a closed-formula model having at least one adjustable parameter.

3. The method of claim 2, wherein the probability distribution function forms a shape controlled by a single parameter directly related to a peak value of the quantized coefficients.

4. The method of claim 1, wherein the mathematical transform is a Laplacian transform.

5. The method of claim 1, wherein the probability density function is fitted to a histogram of quantized transform coefficients for all incoming blocks of samples.

6. The method of claim 1, wherein the probability distribution function is controlled by a maximum absolute value for the blocks of samples.

7. A modeling system having frequency-domain transform coefficients encoded by an encoder that encodes incoming blocks of samples of an input signal for producing quantized coefficients, the modeling system adapted for parametrically modeling source symbols of the encoder and comprising a model processor preprogrammed to compute a probability distribution function by building a mathematical transform and an exponential probability density function wherein the probability distribution function uses a combination of laplacian and exponential models that are fitted to incoming blocks of the samples and to produce a dictionary of input strings from symbol probabilities based on the computed probability distribution function.

8. The modeling system of claim 7, wherein the model processor is preprogrammed to utilize a single parameter determined from a maximum value of the quantized coefficients by utilizing a closed-formula model having at least one adjustable parameter.

9. The modeling system of claim 8, wherein the probability distribution function forms a shape controlled by a single parameter directly related to a peak value of the quantized coefficients.

10. The modeling system of claim 7, wherein the mathematical transform is a Laplacian transform.

11. The modeling system of claim 7, wherein the probability density function is fitted to a histogram of quantized transform coefficients for all incoming blocks of samples.

12. The modeling system of claim 7, wherein the probability distribution function is controlled by a maximum absolute value for the blocks of samples.

13. A method for parametrically modeling source symbols of encoded signals comprising:

quantizing run-length encoded frequency-domain transform coefficients of the signals that comprise blocks of samples;

building a mathematical transform and an exponential probability density function wherein the probability distribution function uses a combination of laplacian and exponential models fitted to each block of the samples; and

using the probability distribution function to model the source symbols to enhance processing efficiency of the encoded signals.

14. The method of claim 13 wherein the transform coefficients are partially whitened.

15. The method of claim 13 wherein a dictionary of input strings is produced from symbol probabilities based on the computed probability distribution function.

16. The method of claim 13 wherein the computed parametric model is unique for each block of samples.

17. The method of claim 15 wherein the shape of the probability distribution function is computed using a single parameter which is directly related to a peak value of the quantized coefficients for each block of samples.

18. The method of claim 13 wherein the probability distribution function is computed using a modified Laplacian transform and an exponential probability density function to uniquely fit the probability distribution function to the histogram of the quantized transform coefficients for each incoming block of samples.

19. The method of claim 13 wherein a quantization step size used for quantization of the transform coefficients is decided by employing a binary search procedure for determining the optimal quantization step size.

20. The method of claim 13, wherein the probability distribution function is controlled by a maximum absolute value for the blocks of transform coefficients.