Interactive image decompression
An important consideration when dealing with large images is the ability to:
- Interactively decompress a subset region of a compressed image.
- Interactively decompress to a level of detail (LOD) of a compressed image. For example, the compressed image might be 200,000x300,000 pixels in size, yet your viewing window might cover 800x600 pixels on screen. In this case, it is important to decompress only the top levels of detail, down to 800x600 pixels in resolution, as that is all the information that can be displayed at the current level of detail on screen.
Doing interactive image decompressing using ER Mapper Compressed Wavelet (ECW) images is simple - your software application automatically decompress the portion of the image currently being viewed, at the level of detail required, on the fly. This is managed by the ER Mapper decompression engine, which manages the display of the imagery within the application such as WORD, ER Mapper or AutoCAD MAP.
ER Mapper ECW V2.0 compressed images maintain the full geocoding and locational information about the file. This means that compressed images will be automatically registered with other imagery and vector data, just like an uncompressed file.
Full real time roaming, zooming and panning is supported in ER Mapper for compressed images, and all the normal algorithm capabilities, such as selecting imagery within a vector polygon, or using statistics, all operate fully.
RAM requirements during interactive viewing/decompression
The ER Mapper Compressed Wavelet (ECW) format has a very low memory footprint during imagery decompression. About 2MB of RAM will be used during interactive decompression of imagery, thus compressed imagery can be used even on quite small computers with limited amounts of RAM - the ER Viewer will view compressed imagery on a machine with a total of 16MB of RAM.
Comparing different compression techniques
Wavelet versus older compression techniques
Over the years, considerable effort has been spent in compressing information in order to make it easy to distribute, and to reduce storage requirements.
Techniques used by common compression program such as ZIP, or by older compression formats such as JPEG or TIFF, suffer from sever disadvantages when trying to compress large digital images. These problems include:
- Limited Compression rates. Because ZIP and related methods are lossless techniques (it is important that a compressed program is exactly compressed), they don't take advantage of the relatively lossy compression techniques that result in much higher compression rates for digital imagery. Typically a "ZIPpend" imagefile will be 50%smaller than the original mage, whereas a wavelet compressed image will be 95% smaller than the original image.
- All or nothing decompression. It is essential to be able to selectively decompress a portion of digital imagery while viewing the image, and to selectively decompress the image, at different levels of detail (as the user zooms in or out of the image view). Older techniques used by ZIP, TIFF, JPEG and other image compression formats, were not designed with selective decompression of imagery in mind. This means that they can not effectively be used when working with images larger than about half computer memory size, which is significantly smaller than the typical size of digital imagery today.
- Artifacts and visible errors in the data. Older techniques, for example as currently used by the JPEG compression process, compress the image as a series of blocks. This is because older techniques were memory based, so they needed to limit the size of each block. Because of this, JPEG and related formats suffer from significant and visible degradations when higher compression ratios are used. Recent breakthroughs in wavelet processing have removed memory limits from image compression. This means that large imagery can be compressed efficiently, without introducing visible artifacts into the compressed image.
- No geographic coordinates. Older image formats do not have, or have very limited, geographic information stored with the compressed image. This is because older formats were designed for graphics art imagery rather than earth related digital imagery such as airphotos or satellite images. Because of this, many formats don't support geographic information, or only do so in a very limited way.
- Slow speed. In order to work effectively with large digital images, the user needs to be able to view any subsection of the image, at any zoom factor, with sub-second response times. Older formats do not achieve adequate response rates for several reasons.
- Many formats require decompressing of the entire image, rather than a selected subset at a select level of detail.
- Many formats assume the image will be smaller than computer RAM, making them very slow to access large images.
- Most formats do not employ "clustering" of imagery information to reduce disk seeks.
Wavelet based image compression and decompression
Wavelet compression involves a way of analyzing an uncompressed image in a recursive fashion, resulting in a series of higher resolution images, each "adding to" the information content in lower resolution images.
The primary step in wavelet compression are performing a Discrete Wavelet Transformation (DWT), quantization of the wavelet-sace image subbands, and then encoding these sub bands.
Wavelet images by and of themselves are not compressed images, rather it is the quantization and encoding stages that do the image compression. Image decompression, or reconstruction, is achieved by carrying out the above steps in reverse and inverse order. Thus, to restore the original image, the compressed image is decoded, dequantized, and then an inverse-DWT is performed.
Because wavelet compression inherently results in a set of multi-resolution images, it is well suited to work with large imagery which needs to be selectively viewed at different resolutions, as only the levels containing the required level of detail need to be decompressed. Wavelet mathematics embraces an entire range of methods each offering different properties and advantages. For example, it is possible to compress 3 or more dimensional imagery using wavelets.
Wavelet compression has not been widely used because the DWT operation takes a lot of compute power, and because historical techniques perform the DWT operation in memory or by storing intermediate results on hard disk. This limits either the size of the image that can be compressed, or the speed at which it can be compressed.
The ER Mapper 6.1 ECW V2.0 wavelet compression uses a breakthrough new technique for performing the DWT and inverse-DWT operations (patent pending), which makes the use of wavelet based compression a practical reality.
Sample applications for ER Mapper Compressed Wavelet imagery
Compression, Storage then selective decompression
You can include compressed imagery in GIS applications using free imagery plugins for GIS and office application with your compressed data. These plugins read the compressed imagery in a wide range of software applications such as ArcView, AutoCAD MAP, MapInfo, ER Mapper, ER Viewer, Photoshop, Microsoft Office and Excell, and other software applications. There is no data royalty fee when using the ER Mapper Compression Wizard to compress images. You can also use the free ECW compression and decompression SDKs to create your own applications that can open, read and write compressed images.
Because the compressed imagery is constructed of multi-resolution wavelet levels, you also enjoy fast roaming and zooming on the imagery-even from slow media such as CD ROM.