Matching pursuit, introduced by Mallat and Zhang, is an algorithm for decomposing a signal into a linear combination of functions chosen from possibly redundant dictionary of functions. A variant which we call quantized matching pursuit has been proposed for various lossy compression problems. Here a simple dependent coding scheme is introduced to code the coefficients and indices in a quantized matching pursuit representation. The improvement in rate-distortion performance is shown through simulations on synthetic sources. The resulting systems is used to code still images and motion-compensated video residual images. Since a DCT-basis dictionary is used, the multiplicative computational complexity is equal to that of traditional transform coding. The image coding results are ambiguous, with a very slight increase in PSNR but no discernible subjective improvement. The video coding results are more promising, with bit rate reductions of up to 20 percent comparing at constant SNR. The competitive performance and design flexibility indicate that the method warrants further investigation.