# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 4 Experimental Results

Table 1: PSNR and Embedding Capacity (EC) according to different thresholds \(T\). same PSNR (31.7 dB), which is 0.1 dB lower than that of [PERSON] and [PERSON]'s method [29]. However, in this case as well, when considering the amount of EC, our method outperforms the other two methods. When \(T=20\), the PSNR of the proposed method is higher than the previous two methods ([PERSON] and [PERSON] [29] and [PERSON] [33]), and the EC of our method has the highest performance. It can be seen from the simulation results in Table 1 that the proposed method has 140,000 bits more than that of [PERSON] and [PERSON] [30] in terms of EC. Figure 8 shows the performance comparison between our proposed method and the existing methods, where we measured the PSNR while increasing the capacity of the secret bits from 20,000 bits to 310,000 bits in four images ((a) Lena, (b) Boat, (c) Pepper, and (d) Zelda) by using the proposed and existing methods. We propose a way to improve the performance of [PERSON] and [PERSON]'s method [30], and as shown in Figure 8, it is confirmed that our proposed method is superior to existing methods. On the other hand, our proposed method shows almost the same performance as [PERSON]'s method [33], but it can be confirmed that the performance of our proposed scheme is slightly better. [PERSON] and [PERSON]'s method [29] is superior to [PERSON] and [PERSON]'s method [20], but the performance is not as high as that of our proposed method. AMBTC has difficulty hiding enough data, because it is a compressed code, and unlike conventional grayscale images, it is not easy to exploit high embedding capacity by the constraint of compressed pixels. It is difficult to improve the DH performance for images with many complex blocks, and if we exploit many pixels for high data concealment, the image quality may deteriorate. Figure 8: Performance comparisons of the proposed method and other related methods (i.e., [PERSON] and [PERSON], [PERSON], Hong) based on four images: (**a**) Lena, (**b**) Boat, (**c**) Pepper, and (**d**) Zelda. In Figure 8, we can see that the EC of [PERSON] and [PERSON]'s method [30] is very low. That is because this method can hide only six bits of data while inverting up to two pixels in each bitmap. Thus, there is a limit to embedding enough data in the _trio_'s bitmaps. Since this method cannot conceal many secret bits for the threshold \(T\) of the same condition, it shows a relatively high PSNR. After all, that is why this method is inferior to other methods. If we would like to increase the number of secret bits even at the expense of the PSNR, it is possible to increase the size of the threshold \(T\). However, it may often be the case that the PSNR becomes worse than expected without increasing the number of hidden bits. For example, when \(T\leq 4\), the three methods except [PERSON] and [PERSON]'s method can hide about 130,000 bits, while the PSNRs are slightly reduced. For such a large amount of data to embed, they exploited the DBS method with respect to \(BM\) equally. [PERSON] and [PERSON]'s method must increase the \(T\) value in order to conceal 130,000 bits of data, and as a result, the errors accumulate rapidly. Since [PERSON] and [PERSON]'s method [30] uses up to four LSB for data concealment, the size of the error inevitably increases. Since our proposed method uses up to three LSB and the frequent count of three LSB is also not very high, the negative effect on image quality is less than that of [PERSON] and [PERSON]'s method [30]. In the end, we prove that the proposed method has a better optimization performance than [PERSON] and [PERSON]'s method [30]. Figure 9 shows the evaluation by comparing the histograms of stego images generated from the proposed method and existing methods, i.e., [PERSON], Ou and Sun, and [PERSON] and Chang. Here, stego images are generated after concealing 150,000 bits in the cover Lena image by the existing and proposed methods. The pixel value range on the x-axis is [95; 115].