Abstrait

Information Loss Reduction in Data Hiding using Visual Sensing Parameter Training Application

Shahin Shafei

The Human Visual System (HVS) is incredibly variable from one person to another and even under different conditions for the same person. Parameterizing allows for this -personalization� while maintaining the familiar property that, if a visually -fine� image is added to another visually -fine� image, the result should also be -fine.� we find that the separate operations generally work best when the parameter values are the same by insuring a visually pleasing result, this should help to improve image enhancement performance. Similar training methods have been introduced in the past and used for a number of applications. Further, we find that good results can be obtained without training the system for individual images, however by utilizing the training system on a specific problem one may have the best results.