Neural network based method for image halftoning and inverse halftoning
Expert Systems with Applications: An International Journal
Improved dot diffusion by diffused matrix and class matrix co-optimization
IEEE Transactions on Image Processing
Parallel and element-reduced error-diffused block truncation coding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Parallel and element-reduced error-diffused block truncation coding
IEEE Transactions on Communications
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The direct binary search (DBS) algorithm is an iterative method which minimizes a metric of error between the grayscale original and halftone image. This is accomplished by adjusting an initial halftone until a local minimum of the metric is achieved at each pixel. The metric incorporates a model for the human visual system (HVS). In general, the DBS time complexity and halftone quality depend on three factors: the HVS model parameters, the choice of initial halftone, and the search strategy used to update the halftone. Despite the complexity of the DBS algorithm, it can be implemented with surprising efficiency. We demonstrate how the algorithm exploits the model for the HVS to efficiently yield very high quality halftones.