## 4 Experimental Results
### Embedding Capacity and Visual Quality Analysis
[1] & & & & & & & & & & & & & & & \\ [PERSON] et al. From the experiments as shown in Table 4, the average embedding capacity obtained from the proposed method is higher than in [PERSON] et al. [7], [PERSON] et al. [11], and [PERSON] et al. [15]. The proposed method is able to embed a payload from 823,499 bits (or 805 KB) to 872,136 bits (or 852 KB). This achieved the embedding bits per pixel ranging from 3.14 to 3.33 bpp with PSNR values from 41.59 to 42.66 dB. However, the [PERSON] et al. and [PERSON] et al. methods had the lower average payload of 820,741 bits (or 802 KB) and 815,682 bits (or 797 KB), respectively. The [PERSON] et al. method resulted in an average payload of approximately 765,120 bits (or 748 KB). From Table 4, it was proved that the proposed method obtained the highest average embedding capacity of 849,347 bits (or 830 KB) and average 3.24 bpp. We can say the proposed method is able to embed 28,606, 33,664, and 84,227 more bits than the [PERSON] et al., [PERSON] et al., and [PERSON] et al. methods, respectively. As seen in Table 4, the proposed method can also perform better to produce less distorted stego-images than existing methods in terms of higher PSNR and a lower number of modified bits per pixel statistics. The proposed method achieved the average modified bits per pixel of 1.87 bits (ranging from 1.69 to 1.98 bits) while existing methods had higher modified bits per pixel, i.e., [PERSON] et al. with 2.25, [PERSON] et al. with 2.31, and [PERSON] et al. with 2.63. Generally, the lower modification bits per pixel value produces a less distorted stego-image which indirectly helps to enhance the PSNR value. Hence, it is proved, and as shown in Table 4 statistics, that the proposed method yielded higher average PSNR of 42.15 dB against the compared methods. The proposed method has a PSNR value 2.27 dB higher than achieved by [PERSON] et al., 2.86 dB higher than [PERSON] et al., and 3.74 dB higher than the [PERSON] et al. method. Finally, the performance of embedding capacity and PSNR are compared for the complete 301 image samples in the USC-SIPI dataset, categorized in four volumes, i.e., Textures, Aerials, Miscellaneous, and Sequences as shown in Table 5. The experimental results show that the proposed method's average embedding capacity is higher than that of the existing methods as highlighted in bold font. Similarly, the PSNR of the proposed method is the highest among others in all tested image resolutions. The overall results demonstrate that the proposed method has better embedding capacity while maintaining higher visual quality and requires a lower number of bits per pixel modification as compared to existing methods. The highest capacity achieved in a proposed method is based on its internal data bits mapping and indicator strategy. It can be seen that the proposed method indirectly embeds up to 4 secret bits per pixel while limiting the modification of bits per pixel up to 2. The statistics in Figure 4 show the comparisons of proposed and recent data embedding approaches. The embedding capacity of the proposed method is far better than that of all the other compared methods except [PERSON] et al. [17], where the PSNR value of [PERSON] et al. [20] is too low (\(-\)13 dB) as compared to the proposed method. Similarly, the proposed method achieved the higher PSNR value while retaining the lower number of (only 2) LSBs used. However, [PERSON] [22] utilized only 1 LSB, but the embedding capacity and visual quality statistics are quite low as compared to the proposed method. In conclusion, we can say the proposed method outperformed all the compared methods in terms of embedding capacity, visual quality and maintaining the minimum no. of LSBs employed for the embedding process.