System and methods for data compression and nonuniform quantizers
AssigneeCable Television Laboratories, Inc.
InventorsMu Xu, Zhensheng Jia, Jing Wang, Luis Alberto Campos
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Abstract
A method for differentiator-based compression of digital data includes (a) multiplying a tap-weight vector by an original data vector to generate a predicted signal, the original data vector comprising N sequential samples of an original signal, N being an integer greater than or equal to one, (b) using a subtraction module, subtracting the predicted signal from a sample of the original signal to obtain an error signal, (c) using a quantization module, quantizing the error signal to obtain a quantized error signal, and (d) updating the tap-weight vector according to changing statistical properties of the original signal.
Claims19
A method for compression of digital data, comprising:
- generating a predicted signal from a quantized sample of an original signal using a least mean square (LMS)-based filtering module;
- subtracting the predicted signal from the original signal to generate an error signal;
- and
- quantizing the error signal to obtain a quantized error signal.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- quantizing the error signal to obtain a quantized error signal;
- and
- adapting the LMS-based filtering module according to changing statistical properties of the original signal.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- and
- quantizing the error signal to obtain a quantized error signal at least partially by using a non-uniform quantizing algorithm.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- and
- quantizing the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module implements a finite impulse response (FIR) filter.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- and
- quantizing the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module is configured to generate a least mean square of the error signal.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- and
- quantizing the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module implements one of a third-order filter and a fourth-order filter.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- quantizing the error signal to obtain a quantized error signal;
- and
- modulating a carrier signal according to the quantized error signal.
A method for compression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- subtracting the predicted signal from an original signal to generate an error signal;
- quantizing the error signal to obtain a quantized error signal;
- and
- sampling an analog signal to generate the original signal.
A method for decompression of digital data, comprising:
- generating a predicted signal from a quantized original signal using a least means square (LMS)-based filtering module;
- and
- adding the predicted signal to a quantized error signal to obtain the quantized original signal.
A method for decompression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- and
- adding the predicted signal to a quantized error signal to obtain a quantized original signal;
- wherein the LMS-based filtering module implements a finite impulse response (FIR) filter.
A method for decompression of digital data, comprising:
- generating a predicted signal using a least means square (LMS)-based filtering module;
- and
- adding the predicted signal to a quantized error signal to obtain a quantized original signal;
- wherein the LMS-based filtering module implements one of a third-order filter and a fourth-order filter.
A data compression system, comprising:
- a least means square (LMS)-based filtering module configured to generate a predicted signal from a quantized sample of an original signal;
- a subtraction module configured to subtract the predicted signal from the original signal to generate an error signal;
- and
- a quantization module configured to quantize the error signal to obtain a quantized error signal.
A data compression system, comprising:
- a least means square (LMS)-based filtering module configured to generate a predicted signal;
- a subtraction module configured to subtract the predicted signal from an original signal to generate an error signal;
- and
- a quantization module configured to quantize the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module is further configured to change constituent tap weight vectors according to changing statistical properties of the original signal.
A data compression system, comprising:
- a least means square (LMS)-based filtering module configured to generate a predicted signal;
- a subtraction module configured to subtract the predicted signal from an original signal to generate an error signal;
- and
- a quantization module configured to quantize the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module is further configured to implement a finite impulse response (FIR) filter.
A data compression system, comprising:
- a least means square (LMS)-based filtering module configured to generate a predicted signal;
- a subtraction module configured to subtract the predicted signal from an original signal to generate an error signal;
- and
- a quantization module configured to quantize the error signal to obtain a quantized error signal;
- wherein the LMS-based filtering module is further configured to implement one of a third-order filter and a fourth-order filter.
A data compression system, comprising:
- a least means square (LMS)-based filtering module configured to generate a predicted signal;
- a subtraction module configured to subtract the predicted signal from an original signal to generate an error signal;
- and
- a quantization module configured to quantize the error signal to obtain a quantized error signal;
- wherein the quantization module is further configured to use a non-uniform quantizing algorithm to quantize the error signal.
Dependencies are inferred from the claim text.