Understanding perceptual distortion in MPEG scalable audio coding

In this paper, we study coding artifacts in MPEG-compressed scalable audio. Specifically, we consider the MPEG advanced audio coder (AAC) using bit slice scalable arithmetic coding (BSAC) as implemented in the MPEG-4 reference software. First we perform human subjective testing using the comparison category rating (CCR) approach, quantitatively comparing the performance of scalable BSAC with the nonscaled TwinVQ and AAC algorithms. This testing indicates that scalable BSAC performs very poorly relative to TwinVQ at the lowest bitrate considered (16 kb/s) largely because of an annoying and seemingly random mid-range tonal signal that is superimposed onto the desired output. In order to better understand and quantify the distortion introduced into compressed audio at low bit rates, we apply two analysis techniques: Reng bifrequency probing and time-frequency decomposition. Using Reng probing, we conclude that aliasing is most likely not the cause of the annoying tonal signal; instead, time-frequency or spectrogram analysis indicates that its cause is most likely suboptimal bit allocation. Finally, we describe the energy equalization quality metric (EEQM) for predicting the relative perceptual performance of the different coding algorithms and compare its predictive ability with that of ITU Recommendation ITU-R BS.1387-1.