A generic transform/wavelet compression system is illustrated in Figure 1. In this system, a transform such as the DCT or a wavelet decomposition is first applied to the image. The resulting transform/wavelet coefficients are then quantized and coded to produce the compressed bitstream. The goal of perceptual image coding is not to achieve the best SNR but to produce an image with the best visual fidelity at the target bitrate. To accomplish this goal, the quantizer need to be both frequency and spatially adaptive. As an example of frequency adaptation, DCT based compression systems such as JPEG use a frequency weighted table, Q-table, to adjust the relative value of the quantization stepsizes. The weights typically increase with respect to the frequency to better match the noise masking property of the human visual system (HVS). Similarly, perceptual weighting has also been successfully applied to wavelet based coders, where the frequency bands are weighted based on a wavelet Q-table. As an example of spatial adaptation, the JPEG-Part3 and MPEG allows the Q-table to be scaled by a factor on a block by block basis. Spatial adaptation by scale factor modulation plays two important roles. First it allows the encoder to adaptively quantize each block based on the local spatial characteristics such as edge, texture, flat region, etc. Second it allows the encoder to achieve the target bitrate in a single pass, which is a very desirable feature in a practical coder. In light of this, the wavelet Q-table should also be allowed to change spatially in order to better reflect the HVS's response to various coefficient type, edge, texture, etc. This paper describes how spatial and frequency adaptive scalar quantization can be used in a wavelet coding framework, both embedded and non-embedded, to achieve perceptual coding and single-pass rate-control. The proposed quantization strategy can be easily supported syntactically, yet if offers a powerful tool to optimize wavelet coders.