Blubox Image Compression
Blubox uses the very latest image compression techniques to get even smaller file sizes while still maintaining incredible image quality.
How does it work?
Blubox uses a set of user-configurable compression filters to encode your picture and non-picture files before storing them in a secure container file (called a 'Blubox archive'). Blubox detects the type of files being imported and chooses the optimum compression technique for each data type. The data is then optionally password encrypted where it remains compressed and secure until you need to export it back to its original format. Your pictures can be viewed and printed while still inside the Blubox archive by using the Blubox program or the free Blubox Viewer.
Blubox determines if the imported file is an image file and uses image compression according to the settings supplied by the user. Non-image files are compressed using binary compression which is the same type of compression used by the popular Zip programs.
Blubox achieves compression/quality ratios way beyond those achievable with industry standard compression formats.
Binary Compression
By default, Blubox compresses non-image file data using a binary compression algorithm provided by Xceed (http://www.xceed.com). This type of compression attempts to identify recurring patterns within the data and replace them with unique, shorter patterns. The compressed data contains a dictionary of the original patterns and the corresponding replacement patterns. Decompression is then a matter of simply undoing those replacements found in the dictionary.
Since compressed data is made up of a number of short, unique patterns, re-compressing the same data often produces a negligible to undesired effect. The main benefit of binary compression is that it can be applied to any type of data. However, some types of data generally compress better than others. Text and document files compress well due to the fact that they typically contain highly redundant sequences of values (i.e. words and sentences). A shortcoming of binary compression is that multimedia data (images, videos, music) usually contains a wide range of the seemingly random values which negate the effects of this type of compression. A truly random set of values would not compress at all as there would be no patterns for the compression mechanism to replace.
Image Compression
Different types of compression have been developed to exploit the unique properties of multimedia data. Essentially, image compression takes advantage of the fact that an image can be altered to a certain extent and still convey the same information or meaning as the original image. The human eye only processes a proportion of the detail presented to it, therefore a 'visually lossless' representation of the original data can be produced at a fraction of the original size. Three primary methods of compression for image data are Quantisation, DCT (Discrete Cosine Transform) and Wavelet Transform which can achieve more effective compression than is possible through the use of binary compression.
Quantization operates by first reducing closely grouped pixels of a similar colour to a set of pixels of the same exact colour. By repeating this throughout an image, long patterns are formed by new blocks of same coloured pixels, thereby making the image more suitable for compression. Image compression mechanisms are often configurable to control the point at which the algorithm determines if a group of pixels is similar enough to try to lump together into a single block. A very relaxed setting allows the compression to larger, more compressible blocks while stricter controls will tend to make smaller blocks. Mathematical transforms are then applied to the blocks of data to reduced them further. These compression techniques require that a certain amount of information will be discarded from the image. This is called 'lossy compression' because information is lost from the image during the compression process.