## 4 Experimental Results and Discussion  
To verify the effectiveness of distributed compressive video sensing coding method in photon-counting underwater wireless optical communication system, we used the standard test video sequences \"foreman\" and \"coastguard\" (frame size: 128 \(\times\) 128) as experimental object, with GOP size = 3. The first frame was a key frame, the second frame and the third frame are non-key frames, and the reconstruction algorithm used TVAL3. As a result of the limited length of the water tank, longer distance communication was simulated by attenuating the power of the LED, and the attenuation in the direction of communication was mainly caused by scattering and absorption. Since the communication rate was low and the communication distance was short, scattering was considered the only cause of attenuation, while other impacts caused by scattering were very small and therefore ignored. Therefore, we did not consider the effect of scattering on this experiment, even though it might be somewhat beneficial when some photons are scattered back to the receiver. Instead, we considered the transmitted power having the main effect on the number of received photons. In addition, we simulated the attenuation of light transmission in water by changing the voltage of the LED lamp. Figure 7 shows the BER at different number of photon pulses per bit calculated at the detector, and the baud rate of 100 kbps. The received optical power evaluation index used in this experiment was the number of single photon pulses output by the detector. It can be seen in the figure that the weaker was the attenuation process, i.e., the more photon pulses per bit, the lower was the bit error rate. Figure 6: Optical signal transmission model of underwater optical communication. Figure 8 shows the waveforms of underwater photon-counting communication at different BER, where a is the modulation signal, b is the single photon pulse signal output by the detector, and c is the demodulation signal. It can be seen in the figure that, when the bit error rate was small, the demodulation signal waveform did not have pitting distortion and tailing phenomenon. When the bit error rate was high, the discrete single photon pulse signal became sparse, the number of photon pulses in the high level decreased, and the demodulation signal part presented pitting distortion. Firstly, we used the standard test video sequences \"coastguard\" as experimental object. It does not need distributed compressive video sensing coding operation. Video frames data were directly transmitted through the underwater photon-counting communication system after channel coding. Then, video frames were measured by distributed compressive video sensing coding, and the measured values were transmitted through the underwater communication system after channel coding. Finally, video frames were restored by decoding at the receiver. The experimental results show the comparison of the first three frames of \"coastguard\" at different BER. In Figure 9, the first line of pictures are the original video frames, the second line of pictures are the result of direct transmission of video frames, and the third line of pictures are the result of transmission of video frames after distributed compressive video sensing coding. When the bit error rate was high, directly transmitted video frames data could not receive clear pictures. Figure 8: Waveform of underwater photon-counting communication at different BER: (**a**) BER = 0.1530; and (**b**) BER = 0.0210. Figure 7: BER at different average number of photon pulses per bit. To evaluate the image reconstruction performance subjectively at different average measurement rate, the comparison of the first three frames of the \"foreman\" is shown in Figure 10 when the average measurement rates were 0.4667, 0.5667, 0.6667 and 0.7667. As can be seen in Figure 10, the key frame was the first frame, and the other frames were non-key frames.