We have investigated an improved decoding paradigm for vector quantization of images. In this new decoding method, the dimension of the code vectors in the decoder is higher than that of the input vectors at the encoder, so that the area covered by each output vector extends beyond the input block of pixels into its neighborhood. The image is reconstructed as an overlapping patchwork of output blocks (code vectors), where the pixel values in the lapped region are obtained by summing the corresponding elements of the overlapping code vectors. With a properly designed decoder codebook, this lapped block decoding technique is able to improve the performance of VQ by exploiting the interblock correlation at the decoder. We have developed a recursive algorithm for designing a locally optimal decoder codebook from a training set of images, given a fixed VQ encoder. Computer simulation with both full search VQ and tree structured VQ encoders demonstrated that, compared to conventional VQ decoding, this new decoding technique reproduces images with not only higher SNR, but also better perceptual quality.