Spatial, Temporal, and Statistical Redundancy Within a still image it is possible to exploit spatial redundancy or statistical redundancy, whereas in a moving sequence you can use spatial, temporal, and statistical redundancy. This section details these approaches. Spatial Redundancy Reduction technique used for a number of years that still has applications in high-quality images is Differential Pulse Code Modulation, or DPCM (Kondoz, 1994). DPCM codes the difference between the pixel of interest and a prediction. The prediction is usually based on the neigh- borhood of the pixel of interest, and it is the error in the prediction that is coded rather than the value of the incoming pixel. The operation is straightforward and illustrated in Figure 3.5. The output of the system, which represents the difference between the prediction and the incoming pixel, does not in itself represent a data reduction, but with a good predictor the output DPCM will have a small dynamic range. Data reduction/compression can be achieved with the addition of an entropy coder that assigns shorter codewords to commonly occurring symbols. If the prediction strategy is appropriate, then compression is achieved, but otherwise expansion can result. A DPCM encoder is shown in Figure 3.5, and the decoder is shown in Figure 3.6. A similar process but in reverse is applied at the decoder, allowing the original bitstream to be recovered. If a quantizer is included in the path, then coefficients will be forced to reside in discrete levels; there is the potential for increased compression, but the Incoming PCM DPCM Coded Predictor Fig. 3.5 Simple DPCM encoder. Incoming PCM Decoded Output Predictor Fig. 3.6 Simple DPCM decoder. Spatial, Temporal, and Statistical Redundancy original information can no longer be reconstructed; only a facsimile is possible. The complexity of the prediction and the spatial correlation of the image ultimately dictate compression ratios, which are usually well below 10:1. It should be noted that even if the predictor is c