A major problem with a vector-quantization-based image compression sch
eme is its codebook search complexity. Recently, a new vector quantiza
tion (VQ) scheme called the predictive residual vector quantizer (PRVQ
) was proposed, which gives performance very close to that of the pred
ictive vector quantizer (PVQ) with very low search complexity. This pa
per presents a new variable-rate VQ scheme called the entropy-constrai
ned PRVQ (EC-PRVQ), which is designed by imposing a constraint on the
output entropy of the PRVQ. We emphasized the design of the EC-PRVQ fo
r bit rates ranging from 0.2 to 1.00 bits per pixel. This corresponds
to compression ratios of 8 through 40, which is the range likely to be
used by most of the real-life applications permitting lossy compressi
on. The proposed EC-PRVQ is found to give a good rate-distortion perfo
rmance and clearly outperforms the state-of-the-art image compression
algorithm developed by the Joint Photographic Experts Group (JPEG). Th
e robustness of the EC-PRVQ is demonstrated by encoding several test i
mages taken from outside the training data. The EC-PRVQ not only gives
better performance than JPEG, at a manageable encoder complexity, but
also retains the inherent simplicity of a VQ decoder.