IP Library Granted Patent US 10,122,379
Granted Patent B1
US 10,122,379 · App. 15/078,229 · Granted Nov 6, 2018

Content-aware compression of data with reduced number of class codes to be encoded

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Quick Facts
Patent No.
US 10,122,379
App. No.
15/078,229
Granted
Nov 6, 2018
Kind
B1
Abstract

Methods and apparatus are provided for content-aware compression of data. An exemplary method comprises obtaining a plurality of floating point numbers each comprising a sign, an exponent at a given base and a significand; grouping a plurality of exponents of the plurality of floating point numbers using a transformation that provides a transformed exponent to reduce a number of distinct exponents in the plurality of floating point numbers to be encoded; and encoding the given floating point number by encoding the transformed exponent and the length of the given floating point number as a single class code. A substantially optimal number of output class codes that need to be encoded is optionally automatically selected. A linear prediction algorithm, such as a first derivative or a second derivative, is optionally applied to the floating point numbers to generate a prediction, wherein the first or second derivative is selected based on an analysis of the data to be compressed.

Claims (31)

1. A method for compressing at least one floating point number, said method comprising:

obtaining a plurality of said floating point numbers, wherein a given floating point number is represented using one or more bits to indicate a sign of said given floating point number and one or more additional bits to indicate an exponent at a given base and a significand of said given floating point number, wherein said significand has a length equal to a number of bits between a most significant bit of said significand and a least significant bit of said significand having a predefined binary value;

transforming, using at least one processing device, a plurality of exponents of said plurality of floating point numbers to generate a transformed exponent to reduce a number of distinct exponents in said plurality of floating point numbers to be encoded; and

encoding, using said at least one processing device, said given floating point number by encoding said transformed exponent and said length of said given floating point number as a single class code.

2. The method of claim 1 , wherein said transformation comprises one or more of a delta encoding and a modulo operation.

3. The method of claim 1 , further comprising the step of applying a linear prediction algorithm to said at least one floating point number to generate a prediction, wherein said linear prediction algorithm comprises one or more of a first derivative and a second derivative.

4. The method of claim 3 , wherein one of said first derivative and said second derivative are selected based on an analysis of a statistically significant portion of data to be compressed.

5. The method of claim 1 , further comprising the step of automatically selecting a number of output class codes that need to be encoded.

6. The method of claim 5 , further comprising the steps of determining an entropy of a current set of classes and determining whether said entropy has increased with respect to an entropy value computed in a previous iteration.

7. The method of claim 1 , wherein said encoding step generates a class code, sign and significand bits for said given floating point number, and wherein said method further comprises the step of applying an entropy encoder to only said class code.

8. The method of claim 1 , further comprising the step of decompressing said compressed given floating point number by decompressing the class codes encoded with said entropy encoder, applying a reverse transformation of said transformation to reduce said number of distinct exponents and restoring discarded bits based on said encoded length.

9. The method of claim 1 , wherein said given floating point number comprises one or more of seismic data, signal data and sensor data.

10. A computer program product for compressing at least one floating point number, comprising a non-transitory machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

obtaining a plurality of said floating point numbers, wherein a given floating point number is represented using one or more bits to indicate a sign of said given floating point number and one or more additional bits to indicate an exponent at a given base and a significand of said given floating point number, wherein said significand has a length equal to a number of bits between a most significant bit of said significand and a least significant bit of said significand having a predefined binary value;

transforming, using said at least one processing device, a plurality of exponents of said plurality of floating point numbers to generate a transformed exponent to reduce a number of distinct exponents in said plurality of floating point numbers to be encoded; and

encoding, using said at least one processing device, said given floating point number by encoding said transformed exponent and said length of said given floating point number as a single class code.

11. The computer program product of claim 10 , further comprising the step of applying a linear prediction algorithm to said at least one floating point number to generate a prediction, wherein said linear prediction algorithm comprises one or more of a first derivative and a second derivative, and wherein one of said first derivative and said second derivative are selected based on an analysis of a statistically significant portion of data to be compressed.

12. The computer program product of claim 10 , further comprising the step of automatically selecting a number of output class codes that need to be encoded.

13. The computer program product of claim 10 , wherein said encoding step generates a class code, sign and significand bits for said given floating point number, and wherein said computer program product further comprises the step of applying an entropy encoder to only said class code.

14. The computer program product of claim 10 , further comprising the step of decompressing said compressed given floating point number by decompressing the class codes encoded with said entropy encoder, applying a reverse transformation of said transformation to reduce said number of distinct exponents and restoring discarded bits based on said encoded length.

15. A system for compressing at least one floating point number, comprising:

a memory; and

at least one processing device, coupled to the memory, operative to implement the following steps:

obtaining a plurality of said floating point numbers, wherein a given floating point number is represented using one or more bits to indicate a sign of said given floating point number and one or more additional bits to indicate an exponent at a given base and a significand of said given floating point number, wherein said significand has a length equal to a number of bits between a most significant bit of said significand and a least significant bit of said significand having a predefined binary value;

transforming, using said at least one processing device, a plurality of exponents of said plurality of floating point numbers using a transformation that provides to generate a transformed exponent to reduce a number of distinct exponents in said plurality of floating point numbers to be encoded; and

encoding, using said at least one processing device, said given floating point number by encoding said transformed exponent and said length of said given floating point number as a single class code.

16. The system of claim 15 , further comprising the step of applying a linear prediction algorithm to said at least one floating point number to generate a prediction, wherein said linear prediction algorithm comprises one or more of a first derivative and a second derivative, and wherein one of said first derivative and said second derivative are selected based on an analysis of a statistically significant portion of data to be compressed.

17. The system of claim 15 , further comprising the step of automatically selecting a number of output class codes that need to be encoded.

18. The system of claim 15 , wherein said encoding step generates a class code, sign and significand bits for said given floating point number, and wherein said system further comprises the step of applying an entropy encoder to only said class code.

19. The system of claim 15 , further comprising the step of decompressing said compressed given floating point number by decompressing the class codes encoded with said entropy encoder, applying a reverse transformation of said transformation to reduce said number of distinct exponents and restoring discarded bits based on said encoded length.

20. The system of claim 15 , wherein said given floating point number comprises one or more of seismic data, signal data and sensor data.

Assignments (7)
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (053546/0001) Recorded Jun 23, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL MARKETING L.P. (ON BEHALF OF ITSELF AND AS SUCCESSOR-IN-INTEREST TO CREDANT TECHNOLOGIES, INC.); DELL INTERNATIONAL L.L.C.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; DELL MARKETING CORPORATION (SUCCESSOR-IN-INTEREST TO FORCE10 NETWORKS, INC. AND WYSE TECHNOLOGY L.L.C.); EMC IP HOLDING COMPANY LLC
Reel/Frame 071642/0001 →
RELEASE OF SECURITY INTEREST AT REEL 048825 FRAME 0489 Recorded Nov 2, 2021
From: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
To: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC
Reel/Frame 058000/0916 →
SECURITY AGREEMENT Recorded Apr 22, 2020
From: CREDANT TECHNOLOGIES INC.; DELL INTERNATIONAL L.L.C.; DELL MARKETING L.P.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; FORCE10 NETWORKS, INC.; WYSE TECHNOLOGY L.L.C.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 053546/0001 →
SECURITY AGREEMENT Recorded Apr 8, 2019
From: DELL PRODUCTS L.P.; EMC CORPORATION; EMC IP HOLDING COMPANY LLC
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
Reel/Frame 048825/0489 →
SECURITY AGREEMENT Recorded Mar 21, 2019
From: CREDANT TECHNOLOGIES, INC.; DELL INTERNATIONAL L.L.C.; DELL MARKETING L.P.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; FORCE10 NETWORKS, INC.; WYSE TECHNOLOGY L.L.C.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 049452/0223 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 5, 2018
From: EMC CORPORATION
To: EMC IP HOLDING COMPANY LLC
Reel/Frame 047318/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 3, 2016
From: CIARLINI, ANGELO E. M.; PINHO, RÔMULO TEIXEIRA DE ABREU; BORDIGNON, ALEX LAIER
To: EMC CORPORATION
Reel/Frame 038446/0877 →