We describe a nonuniform quantization scheme for JPEG2000 that leverages the masking properties of the visual system, in which visibility to distortions declines as image energy increases. Derivatives of contrast transducer functions convey visual threshold changes due to local image content (i.e. the mask). For any frequency region, these functions have approximately the same shape, once the threshold and mask contrast axes are normalized to the frequency's threshold. We have developed two methods that can work together to take advantage of masking. One uses a nonlinearity interposed between the visual weighting and uniform quantization stage at the encoder. In the decoder, the inverse nonlinearity is applied before the inverse transform. The resulting image- adaptive behavior is achieved with only a small overhead (the masking table), and without adding image assessment computations. This approach, however, underestimates masking near zero crossings within a frequency band, so an additional technique pools coefficient energy in a small local neighborhood around each coefficient within a frequency band. It does this in a causal manner to avoid overhead. The first effect of these techniques is to improve the image quality as the image becomes more complex, and these techniques allow image quality increases in applications where using the visual system's frequency response provides little advantage. A key area of improvement is in low amplitude textures, in areas such as facial skin. The second effect relates to operational attributes, since for a given bitrate, the image quality is more robust against variations in image complexity.