COMPRESSION OF IMAGES REPRESENTED USING MULTI-TRANSFORMS

Constrained image representation employing multi-transforms has been r ecently developed. First, the image is divided into smaller non-overla pping subimages. Each subimage is resolved appropriately into 2-D subs ignals, 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 erro r, which is the difference between the original subimage and the recon structed subimage is minimized by adaptive algorithms. An optimization strategy selects the dominant coefficients from the various domains f or adaptation. An efficient coding technique is presented to code the multi-transform coefficients. An image representation example is prese nted employing the DCT-Haar combination. Objective evaluations are mad e 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 improvemen t of about 3 to 4 dB over using DCT alone. Finally, images, coded at b it rates of 0.44 bits/pixel and 1.23 bits/pixel employing the proposed technique verify the good quality of reconstruction.