LEADER 00000nam  2200349   4500 
001    AAI9819908 
005    20050915142044.5 
008    050915s1997                        eng d 
020    0591715651 
035    (UnM)AAI9819908 
040    UnM|cUnM 
100 1  Alford, Jennifer Reynolds 
245 13 An adaptive iterative halftoning algorithm using a 
       multichannel model of texture perception 
300    233 p 
500    Source: Dissertation Abstracts International, Volume: 58-
       12, Section: B, page: 6719 
500    Supervisor: Theophano Mitsa 
502    Thesis (Ph.D.)--The University of Iowa, 1997 
520    Halftoning has traditionally been plagued by quality 
       tradeoffs. Generally, smoothness comes at the expense of 
       dullness and loss of details and sharpness comes at the 
       expense of graininess. Techniques that seek to achieve a 
       "blue noise" distribution in the error spectrum are widely
       accepted as an acceptable balance of smoothness and 
       sharpness. However, achieving a "blue noise" distribution 
       limits image quality by allowing unconstrained high 
       frequency error without regard to image content. The 
       system developed in this thesis, called the Texture Model 
       Based (TMB) halftone algorithm seeks to improve halftone 
       image quality beyond that achieved by non adaptive blue 
       noise techniques by adapting error distribution based on 
       local image content 
520    The system is comprised of a multichannel model of human 
       visual perception. The model contains a multichannel 
       filter bank, drawn from work in texture discrimination and
       classification, embedded in a nonlinear processing stage. 
       Using this model, feature vectors are defined at every 
       pixel which characterize the perceptual features in a 
       neighborhood about each pixel in the grayscale image. The 
       feature vectors then serve as a guide to choosing an error
       constraint for every pixel that is appropriate for its 
       neighborhood characteristics. Then an iterative halftoning
       algorithm is implemented to select a binary value for each
       grayscale pixel that minimizes the difference between the 
       halftone and the grayscale image in a neighborhood of the 
       pixel weighted by the error metric 
520    In order to assess the feasibility of the system, it was 
       implemented and applied to a varied set of images. The 
       results were assessed both qualitatively, by careful 
       observation of specific images, and quantitatively, by an 
       observer test. Observation indicated that the adaptive 
       method did combine the desirable aspects of smoothness and
       sharpness in the same image. The results were 
       reproductions that rendered smooth regions smoothly and 
       texture and details sharply, while preserving overall 
       contrast. The pleasing nature of the results was supported
       by the results of an observer test. The observer test 
       indicated that the new adaptive TMB halftone method was 
       preferred over two other non adaptive, good quality 
       halftone reproductions, both achieving a blue noise error 
590    School code: 0096 
590    DDC 
650  4 Engineering, Electronics and Electrical 
650  4 Computer Science 
690    0544 
690    0984 
710 20 The University of Iowa 
773 0  |tDissertation Abstracts International|g58-12B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/