Abstract: An system and method of adaptively transporting large data assets to meet desired transfer speed and cost, includes selecting folders containing files having the data, receiving input of the desired transfer speed and cost, selecting a destination memory device, reading a file header of the files for the selected folders to obtain header information, determining a file type and file size based on the header information, determining a large file size threshold based on the desired transfer speed and cost and previously measured transfer speed. When the file size of a file is above the large file size threshold, dividing the file into two or more file parts in accordance with the file type, transferring the one or more files, including any of the file parts, using a number of parallel transport machine instances, measuring a total speed of the transfer, and storing the measured total speed for use as the previously measured transfer speed.

Abstract: An system and method of adaptively transporting large data assets to meet desired transfer speed and cost, includes selecting folders containing files having the data, receiving input of the desired transfer speed and cost, selecting a destination memory device, reading a file header of the files for the selected folders to obtain header information, determining a file type and file size based on the header information, determining a large file size threshold based on the desired transfer speed and cost and previously measured transfer speed. When the file size of a file is above the large file size threshold, dividing the file into two or more file parts in accordance with the file type, transferring the one or more files, including any of the file parts, using a number of parallel transport machine instances, measuring a total speed of the transfer, and storing the measured total speed for use as the previously measured transfer speed.

Abstract: A method and apparatus to compress raw color-filter array (CFA) image data in a mathematically lossless manner using spatial processing techniques. The raw CFA data is transformed using a prediction algorithm and then partitioned into blocks having N rows and M columns. The blocks are clustered into groups using a clustering algorithm. Next, compression is performed on blocks within a cluster before the compressed blocks are further compressed using arithmetic coding. In some implementations, alphabet creation and correlation is performed on the CFA data before prediction transformation, and color-plane correlation is performed after prediction transformation but before partitioning into blocks. This spatial compression method can be performed efficiently on camera to compress data files, reducing the storage and data flow requirements of the camera.

Abstract: A method and apparatus to compress raw color-filter array (CFA) image data in a mathematically lossless manner using spatial processing techniques. The raw CFA data is transformed using a prediction algorithm and then partitioned into blocks having N rows and M columns. The blocks are clustered into groups using a clustering algorithm. Next, compression is performed on blocks within a cluster before the compressed blocks are further compressed using arithmetic coding. In some implementations, alphabet creation and correlation is performed on the CFA data before prediction transformation, and color-plane correlation is performed after prediction transformation but before partitioning into blocks. This spatial compression method can be performed efficiently on camera to compress data files, reducing the storage and data flow requirements of the camera.