Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose an original study of the reconstruction of subband compressed images impaired by channel transmission errors. The method proceeds in two steps:first a detection scheme is applied to determine which coefficients of the subband decomposition have been affected by transmission, and then an estimation step tries to evaluate the erroneous coefficients. In our model, subband coefficients are considered to be drawn from jointly Gaussian random processes. Based on this assumption, conditional statistics can be computed which enable to test the likelihood of a given set of received coefficients with respect to the rest of the image. The detection and estimation processes are derived from these statistics. The method is validated through simulation and visual results are provided. The drawbacks of the method are outlined and explained through the discrepancies beten the Gaussian assumption and real world images, namely around image edges.
['Fabrice Labeau', 'Luc Vandendorpe', 'Benoît Macq']
I am writing a research paper on a idea Gaussian modeling for channel errors diagnosis in image transmission
[{"from": "human", "value": "I am writing a research paper on a idea Gaussian modeling for channel errors diagnosis in image transmission"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose an original study of the reconstruction of subband compressed images impaired by channel transmission errors. The method proceeds in two steps:first a detection scheme is applied to determine which coefficients of the subband decomposition have been affected by transmission, and then an estimation step tries to evaluate the erroneous coefficients. In our model, subband coefficients are considered to be drawn from jointly Gaussian random processes. Based on this assumption, conditional statistics can be computed which enable to test the likelihood of a given set of received coefficients with respect to the rest of the image. The detection and estimation processes are derived from these statistics. The method is validated through simulation and visual results are provided. The drawbacks of the method are outlined and explained through the discrepancies beten the Gaussian assumption and real world images, namely around image edges."}]