A Study of color image data compression

The space which black and white or color images require to be stored introduces one of the biggest problems in the field of Graphic Arts. A solution to this problem is offered through the use of software programs that compress the data of a scanned image. Compressing images without any consideration can create other problems. These problems arise because each image has a different structure . It is possible to classify images into three main categories using as a criterion the frequencies the images contain. The first category includes images that contain high frequencies -a lot of detail and very small uniform areas. The second category includes images with fewer frequencies - less detail and larger uniform areas. The third includes images with low frequencies - just a few (or no) details and large uniform areas. The main goal of this study was to set compression ratio standards according to the structure of the images. A software program that does data compression was used. Three 35 mm slides were used as well. The slides have been chosen carefully so that the main topics were composed of frequencies in distinct ranges. All of the images were scanned at 300 pixels per inch. Then all of the images were compressed at three specific compression ratios ( 5:1 , 8:1 , and 14:1) and then printed. Output size was 5x7 inches, the resolution was 256 dpi, and halftones were 150 lines per inch (LPI). A group of forty people (twenty professionals and twenty novices) compared the control image ( non compressed image) with each of the compressed images. The Chi square test was used to analyze the data. The results indicate that it is acceptable to compress images with low detail (like the image Shaving Material) and medium detail (like the image Three Amigos) up to fourteen to one (14:1), because any loss of data is apparently not detectable by the human eye. On the other hand, images which contain a lot of detail (like the image Doll), can not be compressed using the above (14:1) compression ratio without any loss of information being detected. However these images can be compressed up to 8:1 , and any loss of detail up to this compression ratio will not be detected

Progressive transmission and display of static images

Progressive image transmission has been studied for some time in association with image displays connected to remote image sources, by communications channels of insufficient data rate to give subjectively near instantaneous transmission. Part of the work presented in this thesis addresses the progressive transmission problem constrained that the final displayed image is exactly identical to the source image with no redundant data transmitted. The remainder of the work presented is concerned with producing the subjectively best image for display from the information transmitted throughout the progression. Quad-tree and binary-tree based progressive transmission techniques are reviewed, especially an exactly invertible table based binary-tree technique. An algorithm is presented that replaces the table look-up in this technique, typically reducing implementation cost, and results are presented for the subjective improvement using interpolation of the display images. The relevance of the interpolation technique to focusing the progressive sequence on some part of the image is also discussed. Some aspects of transform coding for progressive transmission are reviewed, intermediate image resolution and most importantly problems associated with the coding being exactly invertible. Starting with the two-dimensional case, an algorithm is developed, that judged by the progressive display image can mimic the behaviour of a linear transform while also being exactly invertible (no quantisation). This leads to a mean/difference transform similar to the binary-tree technique. The mimic algorithm is developed to operate on n-dimensions and used to mimic an eight-dimensional cosine transform. Photographic and numerical results of the application of this algorithm to image data are presented. An area transform, interpolation to disguise block boundaries and bit allocation to coefficients, based on the cosine mimic transform are developed and results presented