The most commonly used image magnification techniques are interpolation based: nearest neighbor, bilinear, and bicubic. The drawbacks of these traditional methods are that images magnified by the simple nearest neighbor method often appear “blocky,” while images magnified by linear and cubic interpolations usually appear “blurry.” In this work, a new technique, which improves the performance of the traditional image magnification methods, is presented. We show how a differential image pyramid is first constructed using traditional interpolation methods, then how a vector quantizer is designed using the pyramidal data. The vector quantizer is a look-up table, termed the interresolution look-up table (IRLUT), which uses the lower resolution image vector as input to find as its output the corresponding higher resolution image vector. The improved image is produced by using the IRLUT's outputs to compensate for the image magnified by the traditional methods. Experimental results which show that images generated by the current method have sharper edges as well as lower reconstruction mean-square errors than those produced by traditional methods are presented.