Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a halftoning-based watermarking method that enables the embedding of a color image into binary black-and-white images. To maintain the quality of halftone images, the method maps watermarks to halftone channels using homogeneous dot patterns. These patterns use a different binary texture arrangement to embed the watermark. To prevent a degradation of the host image, a maximization problem is solved to reduce the associated noise. The objective function of this maximization problem is the binary similarity measure beten the original binary halftone and a set of randomly generated patterns. This optimization problem needs to be solved for each dot pattern, resulting in processing overhead and a long running time. To overcome this restriction, parallel computing techniques are used to decrease the processing time. More specifically, the method is tested using a CUDA-based parallel implementation, running on GPUs. The proposed technique produces results with high visual quality and acceptable processing time.
['Pedro Garcia Freitas', 'Mylène C. Q. Farias', 'Aleteia P. F. Araujo']
I am writing a research paper on a idea Hiding color watermarks in halftone images using maximum-similarity binary patterns
[{"from": "human", "value": "I am writing a research paper on a idea Hiding color watermarks in halftone images using maximum-similarity binary patterns"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a halftoning-based watermarking method that enables the embedding of a color image into binary black-and-white images. To maintain the quality of halftone images, the method maps watermarks to halftone channels using homogeneous dot patterns. These patterns use a different binary texture arrangement to embed the watermark. To prevent a degradation of the host image, a maximization problem is solved to reduce the associated noise. The objective function of this maximization problem is the binary similarity measure beten the original binary halftone and a set of randomly generated patterns. This optimization problem needs to be solved for each dot pattern, resulting in processing overhead and a long running time. To overcome this restriction, parallel computing techniques are used to decrease the processing time. More specifically, the method is tested using a CUDA-based parallel implementation, running on GPUs. The proposed technique produces results with high visual quality and acceptable processing time."}]