## 5 Experimental Results  
Twelve standard test images [41] are used to evaluate the effectiveness of the proposed distributed CS image codec. They are shown in Figure 4. These images are \(512\times 512\) pixels in size and contain different content and textures. The visual reconstruction quality of the reconstructed images is evaluated by the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). The compression efficiency of the codec is evaluated by bits per pixel (bpp). The average measurement rate is the average including both the key and WZ blocks. In Section 4.1, it has been shown that smaller block sizes yield a larger percentage of correlated blocks. Therefore, in these experiments, a block size of \(8\times 8\) pixels is used. A scrambled block Hadamard ensemble (SBHE) sensing matrix is used to obtain the CS measurements, and Daubechies 9/7 wavelets are used as the sparsifying matrix. The SpaRSA [24] algorithm is used as the reconstruction algorithm at the decoder. The proposed adaptive and non-adaptive approach with and without SI at the decoder is compared with two other techniques. The first one is the encoding of image blocks with the same measurement rate, i.e., no distinction of the key and WZ blocks. It is labelled as \"Fixed Rate\" in the results. The second technique is first proposed in [12] and recently used to classify image blocks as compressible or incompressible [11]. It is labelled as the \"SD approach\" in the results. The idea is that if the SD of the CS measurements of an image block is higher than the SD of the full image, then the block is classified as incompressible, and more measurements are required for successful reconstruction. The authors of [12] used the original image data to compute the SD, which is not possible in real-world applications. For the adaptive block selection, \(C=1\) is used. All codecs are coded in MATLAB R2012b and simulations run on an Intel i5 3.6 GHz, Windows 7 Enterprise Edition, 64-bit Operating  
Figure 4: Standard test images with their corresponding titles used in the experiments. System with 4 GB of RAM. For each measurement rate per image, the experiment is run five times, and then, the average is reported. Figure 5 shows the R-D curves for four test images (\"Lenna\", \"Boat\", \"Cameraman\" and \"Couple\"). The proposed adaptive sensing scheme with and without SI at the decoder outperforms all other algorithms for all test images. The non-adaptive scheme without SI is comparable with \"Fixed Rate\" and \"SD Approach\". The non-adaptive scheme with SI improves the reconstruction quality and produces better results than \"Fixed Rate\" and \"SD Approach\". The visual reconstruction quality evaluation for the above four images on the basis of structure similarity index [42] is shown in Figure 6. The visual  
Figure 5: Rate-distortion performance for the test images: (**a**) Lena; (**b**) Boat; (**c**) Cameraman; and (**d**) Couple. quality of the adaptive scheme and the non-adaptive scheme with SI is superior for all test images. Table 1 shows the R-D performance of all twelve images averaged for all measurement rates. Again, the adaptive scheme outperforms all other schemes. The lowest average MR is obtained for \"Cameraman\", \"Girl\" and \"Lena\" with the adaptive scheme; the PSNR is more than 1.5 dB better than other schemes. However, for an image with less intra-image similarity, like \"Mandrill\", the adaptive scheme requires a higher number of measurements than other approaches. Using the proposed SI generation method improves reconstruction performance for both adaptive and non-adaptive encoding. Figure 6: SSIM Index for Test Images, (**a**) Lena; (**b**) Boat; (**c**) Cameraman and (**d**) Couple. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Image** & **Criteria** & \begin{tabular}{l} **SD** \\ **Approach** \\ \end{tabular}  & \begin{tabular}{l} **Fixed** \\ **Rate** \\ \end{tabular}  & \begin{tabular}{l} **Non-Adaptive** \\ **(No SI)** \\ \end{tabular}  &