Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 613-615, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/613/2015/
doi:10.5194/isprsarchives-XL-7-W3-613-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
29 Apr 2015
Cloud Optimized Image Format and Compression
P. Becker, L. Plesea, and T. Maurer Esri, 380 New York St, Redlands, CA, 92373, USA
Keywords: Raster Format, Image Format, Compression, Cloud Storage, MRF, LERC Abstract. Cloud based image storage and processing requires revaluation of formats and processing methods. For the true value of the massive volumes of earth observation data to be realized, the image data needs to be accessible from the cloud. Traditional file formats such as TIF and NITF were developed in the hay day of the desktop and assumed fast low latency file access. Other formats such as JPEG2000 provide for streaming protocols for pixel data, but still require a server to have file access. These concepts no longer truly hold in cloud based elastic storage and computation environments.

This paper will provide details of a newly evolving image storage format (MRF) and compression that is optimized for cloud environments. Although the cost of storage continues to fall for large data volumes, there is still significant value in compression. For imagery data to be used in analysis and exploit the extended dynamic range of the new sensors, lossless or controlled lossy compression is of high value. Compression decreases the data volumes stored and reduces the data transferred, but the reduced data size must be balanced with the CPU required to decompress. The paper also outlines a new compression algorithm (LERC) for imagery and elevation data that optimizes this balance. Advantages of the compression include its simple to implement algorithm that enables it to be efficiently accessed using JavaScript. Combing this new cloud based image storage format and compression will help resolve some of the challenges of big image data on the internet.

Conference paper (PDF, 622 KB)


Citation: Becker, P., Plesea, L., and Maurer, T.: Cloud Optimized Image Format and Compression, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 613-615, doi:10.5194/isprsarchives-XL-7-W3-613-2015, 2015.

BibTeX EndNote Reference Manager XML