COMPRESSION OF IMAGES REPRESENTED USING MULTI-TRANSFORMS
Constrained image representation employing multi-transforms has been recently developed. First, the image is divided into smaller non-overlapping subimages. Each subimage is resolved appropriately into 2-D subsignals, each of which is compactly represented in a specific transform domain. The subimage is efficiently represented by superimposing the dominant components corresponding to the subsignals. The residual error, which is the difference between the original subimage and the reconstructed subimage is minimized by adaptive algorithms. An optimization strategy selects the dominant coefficients from the various domains for adaptation. An efficient coding technique is presented to code the multi-transform coefficients. An image representation example is presented employing the DCT-Haar combination. Objective evaluations are made where it is shown that images represented using the multi-transform technique are more accurate than using the DCT for the same number of retained transform coefficients. Test subimages with a high amount of detail represented using the proposed technique show an SNR improvement of about 3 to 4 dB over using DCT alone. Finally, images, coded at bit rates of 0.44 bits/pixel and 1.23 bits/pixel employing the proposed technique verify the good quality of reconstruction.