Perceptual coders and perceptual metrics

We examine perceptual metrics and use them to evaluate the quality of still image coders. We show that meansquared-error based metrics (such as PSNR) fail to predict image quality when one compares artifacts generated by di erent types of image coders (e.g., block-based, subband, and wavelet coders). We consider three di erent types of coders: JPEG, the Safranek-Johnston perceptual subband coder (PIC), and the Said-Pearlman SPIHT algorithm with perceptually weighted subband quantization, based on the Watson et al. visual thresholds. We show that incorporating perceptual weighting in the SPIHT algorithm results in signi cant improvement in visual quality. The metrics we consider are based on the same image decompositions (subband, wavelet, DCT) as the corresponding compression algorithms. Such metrics are computationally e cient and considerably simpler than more elaborate metrics (e.g., by Daly, Lubin, and Teo and Heeger). However, since each of the metrics is used for the optimization of a coder, one expects that they would be biased towards that coder. We use the metrics to evaluate the performance of the compression techniques for a wide range of bit rates. Our experiments indicate that the PIC metric provides the best correlation with subjective evaluations. It predicts that at very low bit rates the SPIHT algorithm and the 8 8 PIC coder perform the best, while at high bit rates the 4 4 PIC coder is the best. More importantly, we show that the relative algorithm performance depends on image content, with the subband and DCT coders performing best for images with a lot of high frequency content, and the wavelet coders performing best for smoother images.