Methods and apparatuses for variable dimension vector quantization
First Claim
1. A method for creating an optimum partition for a codebook, wherein the codebook includes at least one codevector yi, wherein each of the at least one codevectors yi includes a codevector dimension Nv and at least one codevector element yi,m, comprising:
- (A) collecting a training data set, wherein the training data set comprises a plurality of input vectors, wherein each input vector is denoted xk and includes a variable training vector dimension N(Tk);
(B) defining a partition rule;
(C) defining a distortion measure for the partition rule, wherein the distortion measure defines an average distortion; and
(D) finding a nearest codevector for each of the plurality of input vectors using an interpolation index relationship.
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Abstract
Improved variable dimension vector quantization-related (“VDVQ-related”) processes have been developed that provide quality improvements over known coding processes in codebook optimization and the quantization of harmonic magnitudes that can be applied to a broad range of distortion measures, including those that would involve inverting a singular matrix using known centroid computation techniques. The improved VDVQ-related processes improve the way in which actual codevectors are extracted from the codevectors of the codebook by redefining the index relationship and using interpolation to determine the actual codevector elements when the index relationship produces a non-integer value. Additionally, these processes improve the way in which codebooks are optimized using the principles of gradient-descent. These improved VDVQ-related processes can be implemented in various software and hardware implementations.
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Citations
5 Claims
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1. A method for creating an optimum partition for a codebook, wherein the codebook includes at least one codevector yi, wherein each of the at least one codevectors yi includes a codevector dimension Nv and at least one codevector element yi,m, comprising:
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(A) collecting a training data set, wherein the training data set comprises a plurality of input vectors, wherein each input vector is denoted xk and includes a variable training vector dimension N(Tk);
(B) defining a partition rule;
(C) defining a distortion measure for the partition rule, wherein the distortion measure defines an average distortion; and
(D) finding a nearest codevector for each of the plurality of input vectors using an interpolation index relationship. - View Dependent Claims (2, 3)
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4. A computer readable storage medium storing computer readable program code for creating an optimum partition, the computer readable program code comprising:
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data encoding a codebook and a training data set;
wherein the codebook includes the at least one codevector yi, wherein the at least one codevector yi includes at least one codevector element yi,m; and
wherein the training data asset includes a plurality of input vectors; and
a computer code implementing a method for creating an optimum partition in response to the plurality of input vectors, wherein the method for creating an optimum partition includes;
(A) collecting a training data set, wherein the training data set comprises a plurality of input vectors, wherein each input vector is denoted xk and includes a variable training vector dimension N(Tk);
(B) defining a partition rule;
(C) defining a distortion measure for the partition rule, wherein the distortion measure defines an average distortion; and
(D) finding a nearest codevector for each of the plurality of input vectors using an interpolation index relationship.
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5. An optimum partition creation device for a codebook, wherein the codebook includes at least one codevector yi, wherein each of the at least one codevectors yi includes a codevector dimension Nv and at least one codevector element yi,m, comprising:
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an interface unit for receiving a training data set, a partition rule, and a distortion measure, wherein the training data set includes a plurality of input vectors, wherein the plurality of input vectors includes a variable training dimension N(Tk); and
wherein the distortion measure defines an average distortion; and
a partition creation unit coupled to the interface unit, wherein the partition creation unit includes a memory and a processor coupled to the memory unit;
wherein the memory stores the at least one codevector yi, the distortion measure, the partition rule, and a method for creating an optimum partition for the codebook; and
wherein the processor, using the method for creating the optimum partition for the codebook, the at least one codevector yi, the partition rule and the distortion measure communicated from the memory, finds the nearest codevector for each of the plurality of input vectors using an interpolation index relationship.
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Specification