R-D Optimization of EED-based Compressed Image Using Linear Regression

Image compression technique minimizes the size in bytes of a graphics file without degrading the quality of the image to an unacceptable visual level. The performance is driven by the trade-off between rate (R), distortion (D), and complexity of the coding algorithm. This piece of work deals with the simple yet effective method of image-dependent R-D optimization by optimizing the cost function of the constrained Lagrangian multiplier. A simple linear regression is employed to estimate the cost and the slope of the convex haul in the R-D plane. The image is compressed by SPIHT algorithm after it was subjected to anisotropic diffusion to provide a powerful zero tree pyramid. The Perona-Malik diffusion preserves the edge and boundary information by smoothening the whole image. Extensive simulation for the proposed diffusion platform provides better results in terms of PSNR and visual quality, and the estimation approximates the actual values with minimum residuals.