ADAPTIVE IMAGE ENCODING AND CLASSIFICATION USING NEURAL NETWORKS

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$49,629.00
Award Year:
1989
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
10502
Agency Tracking Number:
10502
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4241 Jutland Dr, San Diego, CA, 92117
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Richard S Cigledy
() -
Business Contact:
() -
Research Institution:
n/a
Abstract
WE INTEND TO EXPLORE THE VIABILITY OF NEURAL NETWORKS FOR IMAGE COMPRESSION AND PATTERN CLASSIFICATION IN AN INTEGRATED SYSTEM. THIS IS A NEW APPROACH TO IMAGE COMPRESSION THAT HAS SEVERAL ADVANTAGES OVER STANDARD APPROACHES. SO FAR, NEURAL NETWORKS HAVE BEEN SHOWN TO BE COMPARABLE TO STANDARD TECHNIQUES (COTTRELL, MUNRO & ZIPSER,1987) FOR THE TASK OF IMAGE COMPRESSION. THEY SHOWED THAT A 3 LAYER BACK PROPAGATION NETWORK (RUMELHART, HINTON & WILLIAMS, 1986) WITH ESSENTIALLY NO TUNING COULD ACHIEVE LEVELS OF COMPRESSION ON THE ORDER OF 1 BIT PER PIXEL (BPP). WE INTEND TO EXTEND THAT WORK IN AN ATTEMPT TO ACHIEVE COMPRESSION RATES BELOW 1 BPP AND INVESTIGATE ITS USEFULNESS IN NASA APPLICATIONS. IF THE ALGORITHM CAN BE EMBEDDED IN HARDWARE, THE POTENTIAL ADVANTAGES FOR SPACE-BASED APPLICATIONS ARE: A COMPRESSION DEVICE THAT ADAPTS TO THE CURRENT ENVIRONMENTAL AND HARDWARE CONDITIONS AND OPERATES IN REAL TIME, LESS SENSITIVITY TO CHANNEL ERRORS, A RECONFIGURABLE PATTERN CLASSIFIER THAT CAN BE TRAINED IN SITU.

* information listed above is at the time of submission.

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