Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A Source Specific Model for Global Earth Data (SSM-GED) is a lossless compression method for large images that captures global redundancy in the data and achieves a significant improvement over CALIC and DCXT-BT/CARP, two leading lossless compression schemes. The Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) data, which contains 662 Megabytes (MB) per band, is an example of a large data set that requires decompression of regions of the data. For this reason, SSM-GED compresses the AVHRR data as a collection of subwindows. This approach defines the statistical parameters for the model prior to compression. Unlike universal models that assume no a priori knowledge of the data, SSM-GED captures global redundancy that exists among all of the subwindows of data. The overlap in parameters among subwindows of data enables SSM-GED to improve the compression rate by increasing the number of parameters and maintaining a small model cost for each subwindow of data.
['Barbara L. Kess', 'Stephen E. Reichenbach']
I am writing a research paper on a idea Capturing global redundancy to improve compression of large images
[{"from": "human", "value": "I am writing a research paper on a idea Capturing global redundancy to improve compression of large images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A Source Specific Model for Global Earth Data (SSM-GED) is a lossless compression method for large images that captures global redundancy in the data and achieves a significant improvement over CALIC and DCXT-BT/CARP, two leading lossless compression schemes. The Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) data, which contains 662 Megabytes (MB) per band, is an example of a large data set that requires decompression of regions of the data. For this reason, SSM-GED compresses the AVHRR data as a collection of subwindows. This approach defines the statistical parameters for the model prior to compression. Unlike universal models that assume no a priori knowledge of the data, SSM-GED captures global redundancy that exists among all of the subwindows of data. The overlap in parameters among subwindows of data enables SSM-GED to improve the compression rate by increasing the number of parameters and maintaining a small model cost for each subwindow of data."}]