Measuring Meaningful Information in Images: A Specified Complexity Algorithm

Winston Ewert, William Dembski, Robert . Marks II, "Measurement of Meaningful Information in Images: A Specified Complexity Algorithm", IET Computer Vision, Part 9 (6): 884-894 (December, 2015). Winston Ewert, William A. Dembski, Robert J. Marks II, “Measuring meaningful information in images: algorithmic specified complexity,” IET Computer Vision, Vol. 9 (6): 884-894 (December, 2015). This rigorous article applies a specific complexity algorithm as a measure of meaning versus randomness within an information packet, providing us with a powerful mathematical analysis tool to investigate the design. The author tested this methodology by comparing "computer images that contain content and distinguish them from images that are simply random or meaningless excess noise." According to this model in investigating the design: “For an image to be meaningfully discernible, it must have something to do with some independent external model or specification. The image of the sunset is meaningful because the viewer empirically associates it with other times when the sun has set. Any image with content other than random noise fits into some comparative model. It is natural for any image to express itself, but it needs to be independent of observation. How can we determine whether an image has a qualitative complexity? The authors of the article explain that it must achieve two things, be non-compressible (complex) and fit into a model: “The more a picture can be described as a model, the more compressible it is, the more qualitative it is. For example, a black square is described as a simple model, and a very short computer program suffices to redesign it. A picture from several random pixels pixel: a small point of a TV image can’t be described as much, if at all, as a static one. The image of a simple black square may be compressible and qualitative, but it is not complicated. The authors predict that “a random image is easily more complex than a solid square.” But these cases are relatively easy. They then tried to process harder images, such as the famous scientist Louis Pasteur’s image with increasing amounts of random noise added. As their specific complexity algorithm predicts, as the noise added to the image its specific complexity algorithm decreased. How about an image of "Simplified Planning for Humans in a Sea of Noise"? They found that the complexity algorithm generated by a given square is able to investigate the existence of complex and qualitative features even when surrounded by noise. They concluded that the complexity algorithm defined is an effective methodology for distinguishing random data in meaningful images: "We estimated the probability of various images by using the number of bits required to encode images in a simple PNG manner. Thus, we demonstrated the theoretical applicability of the specific complexity algorithm to the issue of distinguishing information from noise, and reviewed the methodology by which compressed file sizes can be used to estimate the content of meaningful information for images.” The applicability to intelligent design is clear. If a specific complexity algorithm is a useful tool in distinguishing images designed from random images or from images produced by undirected algorithms, it may be possible to apply it to biological systems or other natural structures to investigate design as well. More: https://braheen.com/publications/التصميم-الذكي-ومراجعة-الأقران — Source: Published on Facebook's "Braheen Center" page