  We show how real-number codes can be used to compress correlated sources, and
establish a new framework for lossy distributed source coding, in which we
quantize compressed sources instead of compressing quantized sources. This
change in the order of binning and quantization blocks makes it possible to
model correlation between continuous-valued sources more realistically and
correct quantization error when the sources are completely correlated. The
encoding and decoding procedures are described in detail, for discrete Fourier
transform (DFT) codes. Reconstructed signal, in the mean squared error sense,
is seen to be better than that in the conventional approach.
