Improved Dot Diffusion and Feature-Classified Inverse Halftoning

• Main Authors: Chia-Hao Chang, 張家豪
• Other Authors: Jing-Ming Guo
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/76130558903436345539

碩士 === 國立臺灣科技大學 === 電機工程系 === 98 === Digital halftoning is a technique that can translate a continuous-tone image to a bi-tone image. It is broadly applied for displaying and printing, etc. The main challenge in this research topic is to provide high image quality while maintain low computational complexity and memory consumption. Two contributions are addressed in this thesis. In the first half, an improved dot diffusion is proposed using hexagonal grid. Currently, dot diffusion is not employed in commercial market for its rather low image quality. The dot diffusion can provide parallel processing advantage with the aid of class matrix and diffused matrix. Yet, the main deficiency of dot diffusion is the annoying blocking effect. For this, the former rectangle shape class matrix is replaced with hexagonal shape for improving image quality. As documented in the experimental results, the blocking effect is significantly reduced. The image quality is even better than some commercial error diffusion schemes. In this work, a set of class matrices and diffused matrices are provided, and the Human Visual System (HVS) is also involved for image quality evaluation to minimize the cost function. Moreover, the Simulated Annealing (SA) is employed for training process to yield the optimized class matrix and diffusion matrix. In the second half, a Feature-Classified Inverse Halftoning (FCIH) is proposed. Inverse halftoning is a technique that can translate a two tone image to a continuous-tone image, which can be used for halftone image compression, such as facsimile transmission. Herein, the pattern variance, dot type, and local average grayscale are considered as significant features. The three features are used to classified adaptive filters for inverse halftoning using Least Mean Square (LMS). Herein, the Maximum Filters Difference Guidance (MFDG) is developed to determine the number of classified filters. As documented in the experimental results, the proposed inverse halftoning shows excellent performance in image quality, memory consumption, and complexity compared with former approaches.