The approximated image model as a describer is automatically generated by the RBFPSO learning scheme, which is contained hybrid bacterial foraging particle swarm optimization (BFPSO) algorithm and recursive least-squares (RLS) iterations to deeply approach the image feature.
By analyzing the relationship between DCT coefficients of one block of 8×8 pixels and its four sub-blocks of 4×4 pixels, the proposed algorithm extract an approximated image with smaller size for indexing and content browsing without incurring full decompression.
While the computing cost is significantly lower than full decompression, the approximated image also reserves the content features, which are sufficient for indexing and browsing as evidenced by our extensive experiments.
The M-S model [3] is a variational problem for approximating an image by a piecewise smooth image of minimal complexity.
The goal of the app is to approximate images taken with a larger aperture (i.e. a synthetic aperture) than that of the cell phone camera.
Let the high, the middle, and the low resolution of our scenario correspond to,, and approximate images, respectively.
To prevent unsharp and approximate images, each projection has to be convolved with a predetermined mathematical function, the convolution kernel; this kernel represents a filtering procedure that is able to enhance or reduce object boundaries.
The proposed method warrants the lower limit of the image quality of a watermarked image by approximating an original image to the corresponding lower limit image for embedding.
