## 5 Experimental Results
### PSNR Comparisons  
To demonstrate the effectiveness of our method, we conducted three experiments for comparing the image qualities of the proposed method with those of [PERSON] et al. [16] and [PERSON] et al. [17] under the same embedding capacity. The first two experiments assume that the location map needed by [PERSON] et al. and [PERSON] et al. 's methods is not embedded in the stego image, and it is sent through another channel. The third experiment assumes that the location map needed by [PERSON] et al. and [PERSON] et al. 's methods is embedded in the stego image; thus, the embedding capacity is reduced. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Size** & \multicolumn{2}{c}{**2 × 2**} & \multicolumn{2}{c}{**1 × 3**} \\ \hline
**Embedding Method** & [16] & **Proposed Method** & [17] & **Proposed Method** \\ \hline Total Block number (\(T\)) & & 16,384 & & 21,845 \\ Location map size (\(X\) bits) & 12,288 & 0 & 16,383 & 0 \\ Block number needed for record & & 4096 & 0 & 5461 & 0 \\ location map (\([X/k]\)) & 12,288 & 16,384 & 16,384 & 21,845 \\ Number of available blocks for secret & & & & \\ data embedding (\(T-[X/k]\)) & 36,864 & 49,152 & 49,152 & 65,535 \\ Maximum embedding capacity (bits) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparisons of embedding capacities among [PERSON] et al.’s [16], [PERSON] et al.’s [17], and the proposed methods for image size \(256\times 256\) and \(k=3\). In the first experiment, [PERSON] et al. 's and the proposed methods with capacities of 15,000 and 30,000 bits were conducted. First, each image was divided into \(128\times 128\) blocks, each of which has size \(2\times 2\). Then, each block was embedded \(k\) bits, \(k=1,\ 2\). Part of results is depicted in Tables 3 and 4. These tables reveal that the image qualities of the proposed method were the best. This means that the proposed method actually has lower embedding distortion for each block such that each block can be used for data embedding. In the following, more details of explanation are described. Figure 1: Twenty-five test images. In [PERSON] et al. 's method, the palette is reordered; this increases the distance between two neighboring colors (i.e., two colors with index difference 1) when their indices are large. In data embedding, if one pixel's color \(c\) with index \(i\) is replaced by color \(c^{\prime}\), then the index of \(c^{\prime}\) is \(i-1\) or \(i+1\). However, when \(i\) is large, these two colors with the index difference of 1 may not be close; this increases the embedding distortion. To provide more explanation, the image Lena was used with setting \(k=1\). Figure 2 illustrates the result of [PERSON] et al. 's parity assignment. Figure 2a depicts all colors with parity 0, and Figure 2c illustrates all colors with parity 1. Figure 2b depicts the corresponding closest color with parity 1 of each color in Figure 2a. Figure 2d depicts the corresponding closest color with parity 0 of each color in Figure 2c. From these figures, we can determine that for each color with parity 0 (1), a similar color with parity 1 (0) can always be found. Figure 3 illustrates the result of [PERSON] et al. 's palette reordering. Figure 3a displays the reordered color palette. Figure 3b denotes the best replacing one of each color in Figure 3a when embedding data. Figure 3c illustrates the enlarged part of the rectangle marked by red color in Figure 3a. Figure 3d depicts the enlarged part of the rectangle marked by red color in Figure 3b. These figures indicate that some colors may be replaced by an unsimil