You might imagine, therefore, that this is an bit image. The camera I used to acquire this image codes up to different intensity levels ( bits). Suppose that we have an image we wish to encode efficiently, such as the image in Figure 8.1a. In the next Chapter 9, which is devoted to color broadly, we will again touch on some of the issues of color image representation. In this chapter we will consider efficient encoding algorithms of monochrome images, spending most of our time on issues of intensity and spatial redundancy. By eliminating this information, we improve the efficiency of the image representation. Representing this information in the stored image is unnecessary because the receiver, that is the visual system, cannot detect it.
People have very poor spatial resolution to short-wavelength light, and only limited spatial resolution for colored patterns in general. Second, we know that human spatial resolution to certain spatial patterns is very poor (see Chapters 2 and 7). This redundancy is part of the signal, and it may be removed without any loss of information in order to obtain more efficient representations. One approach to thinking about visual algorithms, then, is to adopt a general approach to vision, sometimes called in these images. In addition to the intrinsic and practical interest of solving visual problems, finding principled solutions for visual tasks can also be helpful in understanding and interpreting the organization of the human visual pathways.īut, which tasks should we consider? There are many types of visual problems only some of these have to do with tasks that are essential for human vision. We will spend this chapter mainly just thinking about how these and other organizational principles might be relevant to solving various visual tasks. All of the evidence we have reviewed to this point suggest that image contrast, rather than image intensity, is the key variable represented by the visual pathways.
Behavioral evidence suggests that within the streams that are specialized for pattern sensitivity, information is further organized by local orientation and spatial scale and color. Neural evidence suggests that visual information is segregated into a number of different visual streams that are specialized for different tasks. Our review of the organization of neural and behavioral data have led us to several specific hypotheses about how the visual system represents pattern information. Applications of multiresolution representations.Image Compression Using the Error Pyramid.
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