0 Compression Using Context Matching-Based Prediction

   The algorithm presented in this section uses both predictive and entropy coding to com-
press  CFA  data.     First,  the  CFA  image  is  separated  into  the  luminance  subimage  (Fig-
ure 3.1b) containing all green samples and the chrominance subimage (Figure 3.1c) con-
taining all red and blue samples. These two subimages are encoded sequentially. Samples in the same subimage are raster-scanned and each one of them undergoes a prediction pro-
cess based on context matching and an entropy coding process as shown in Figure 3.3a.
Due to the higher number of green samples in the CFA image compared to red or blue
samples, the luminance subimage is encoded before encoding the chrominance subimage.
When handling the chrominance subimage, the luminance subimage is used as a reference
to remove the interchannel correlation.

Figure 3.3

 Decoding is just the reverse process of encoding as shown in Figure 3.3b. The lumi-
nance subimage is decoded first to be used as a reference when decoding the chrominance
subimage. The original CFA image is reconstructed by combining the two subimages.


Context Matching-Based Prediction 

   In the prediction process exploited here, the value of a pixel is predicted with its four
closest processed neighbors in the same sub-image. The four closest neighbors from the
same color channel as the pixel of interest should have the highest correlation to the pixel
to be predicted in different directions and hence the best prediction result can be expected.
These four neighbors are ranked according to how close their contexts are to the context
of the pixel to be predicted and their values are weighted according to their ranking order.
Pixels with closer contexts to that of the pixel of interest contribute more to its predicted
value. The details of its realization in handling the two subimages are given below.