Patent US6148283A successfully refreshed from Google Patents.
US6148283A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Method and apparatus using multi-path multi-stage vector quantizer
Assignee: Qualcomm Inc
•
Filing: 1998-09-23
•
Granted: 2000-11-14
•
Expires: 2018-09-23
Abstract
Abstract A multi-path, split, multi-stage vector quantizer (MPSMS-VQ) having multiple paths between stages which result in a robust and flexible quanitizer. By varying parameters, the MPSMS-VQ meets design requirements, such as: (1) the number of bits used to represent the input vector (i.e., uses the same or less total bits than the given number of bits, N); (2) the dimension of the input vector, the performance (distortion as noted by WMSE or SD); (3) complexity (i.e., total complexity can be adjusted to be within a complexity constraint); and (4) memory usage (i.e., total number of words M in the codebook memory can be adjusted to be equal to, or less than, the memory constraint M d ). Therefore, the disclosed method and apparatus works well in many conditions (i.e., offers a very robust performance across a wide range of inputs).
Inventors
- 👤 Amitav Das
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-31 15:51
CPC Classifications
G; G10; G10L; G10L19/00; G10L19/04; G10L19/06; G10L19/07; G10L2019/0001; G10L2019/0004; G10L2019/0005
Claims Analysis
Total Claims: 7
Claim 1
Independent Claim
79 words
An apparatus for quantizing vectors, comprising: a plurality of split vector quantization codebook stages, each split vector quantization codebook stage having at least two sub-codebooks, there being one sub-codebook for each split of a given split vector quantization codebook stage, wherein a set of best candidate codevectors is selected for each split and from each split vector quantization codebook stage; and a trellis-coded, multipath, backward tracking mechanism for selecting a final codevector from the sets of best candidate codevectors.
Claim 2
Independent Claim
79 words
An apparatus for quantizing vectors, comprising: a plurality of split vector quantization codebook stages, each split vector quantization codebook stage having at least two sub-codebooks, there being one sub-codebook for each split of a given split vector quantization codebook stage, wherein a set of best candidate codevectors is selected for each split and from each split vector quantization codebook stage; and a trellis-coded, multipath, backward tracking mechanism for selecting a final codevector from the sets of best candidate codevectors.
Claim 3
Dependent Claim
118 words
A method of training codevectors for each sub-codebook of each split vector quantization codebook stage in the apparatus of claim 1, comprising the steps of: obtaining an initial set of sub-codebooks; training one sub-codebook while fixing the remaining sub-codebooks of the initial set of sub-codebooks; comparing an input training vector for the one sub-codebook with the final codevector to derive a distortion measure; forming a partition for each current sub-codebook entry of the sub-codebook being trained, the partition comprising a set of training data that minimizes the distortion measure for the sub-codebook entry; updating each partition with a centroid partition; and performing the training, comparing, forming, and updating steps for each sub-codebook to achieve an overall distortion measure.
Claim 4
Dependent Claim
21 words
The apparatus of claim 1, further comprising means for training codevectors for each sub-codebook of each split vector quantization codebook stage.
Claim 5
Dependent Claim
116 words
The apparatus of claim 3, wherein the means for training comprises: means for obtaining an initial set of sub-codebooks; means for training one sub-codebook while fixing the remaining sub-codebooks of the initial set of sub-codebooks; means for comparing an input training vector for the one sub-codebook with a final codevector to derive a distortion measure; means for forming a partition for each current sub-codebook entry of the sub-codebook being trained, the partition comprising a set of training data that minimizes the distortion measure for the sub-codebook entry; means for updating each partition with a centroid partition; and means for performing the training, comparing, forming, and updating steps for each sub-codebook to achieve an overall distortion measure.
Claim 6
Dependent Claim
211 words
In a multistage, multipath, split vector quantizer, the quantizer including a plurality of split vector quantization codebook stages, each split vector quantization codebook stage having at least two sub-codebooks, there being one sub-codebook for each split of a given split vector quantization codebook stage, wherein a set of best candidate codevectors is selected for each split and from each split vector quantization codebook stage; and a trellis-coded, multipath, backward tracking mechanism for selecting a final codevector from the sets of best candidate codevectors, a method of training codevectors for each sub-codebook of each split vector quantization codebook stage, the method comprising the steps of: obtaining an initial set of sub-codebooks, there being at least two sub-codebooks available in each split vector quantization codebook stage; training one sub-codebook while fixing the remaining sub-codebooks of the initial set of sub-codebooks; comparing an input training vector for the one sub-codebook with a final codevector to derive a distortion measure; forming a partition for each current sub-codebook entry of the sub-codebook being trained, the partition comprising a set of training data that minimizes the distortion measure for the sub-codebook entry; updating each partition with a centroid partition; and performing the training, comparing, forming, and updating steps for each sub-codebook to achieve an overall distortion measure.
Claim 7
Dependent Claim
211 words
In a multistage, multipath, split vector quantizer, the quantizer including a plurality of split vector quantization codebook stages, each split vector quantization codebook stage having at least two sub-codebooks, there being one sub-codebook for each split of a given split vector quantization codebook stage, wherein a set of best candidate codevectors is selected for each split and from each split vector quantization codebook stage; and a trellis-coded, multipath, backward tracking mechanism for selecting a final codevector from the sets of best candidate codevectors, a method of training codevectors for each sub-codebook of each split vector quantization codebook stage, the method comprising the steps of: obtaining an initial set of sub-codebooks, there being at least two sub-codebooks available in each split vector quantization codebook stage; training one sub-codebook while fixing the remaining sub-codebooks of the initial set of sub-codebooks; comparing an input training vector for the one sub-codebook with a final codevector to derive a distortion measure; forming a partition for each current sub-codebook entry of the sub-codebook being trained, the partition comprising a set of training data that minimizes the distortion measure for the sub-codebook entry; updating each partition with a centroid partition; and performing the training, comparing, forming, and updating steps for each sub-codebook to achieve an overall distortion measure.
References Cited (Backward Citations)
US5243686A
Cited
US5651026A
Cited
US5535305A
Cited
US5737484A
Cited
US5398069A
Cited
US5408234A
Cited
US5859932A
Cited
US5890110A
Cited
US5822723A
Cited
US5774839A
Cited
US5787390A
Cited
US5966688A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2003-11-14 | 2004-05-15 | 2004-11-14 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2007-11-14 | 2008-05-15 | 2008-11-14 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2011-11-14 | 2012-05-15 | 2012-11-14 | $7700.00 | Window Closed / Lapsed |