  Recently, pseudo analog transmission has gained increasing attentions due to
its ability to alleviate the cliff effect in video multicast scenarios. The
existing pseudo analog systems are sorely optimized under the minimum mean
squared error criterion without taking the perceptual video quality into
consideration. In this paper, we propose a human-perception-based pseudo analog
video transmission system named ROIC-Cast, which aims to intelligently enhance
the transmission quality of the region-of-interest (ROI) parts. Firstly, the
classic deep learning based saliency detection algorithm is adopted to
decompose the continuous video sequences into ROI and non-ROI blocks. Secondly,
an effective compression method is used to reduce the data amount of side
information generated by the ROI extraction module. Then, the power allocation
scheme is formulated as a convex problem, and the optimal transmission power
for both ROI and non-ROI blocks is derived in a closed form. Finally, the
simulations are conducted to validate the proposed system by comparing with a
few of existing systems, e.g., KMV-Cast, SoftCast, and DAC-RAN. The proposed
ROIC-Cast can achieve over 4.1dB peak signal- to-noise ratio gains of ROI
compared with other systems, given the channel signal-to-noise ratio as -5dB,
0dB, 5dB, and 10dB, respectively. This significant performance improvement is
due to the automatic ROI extraction, high-efficiency data compression as well
as adaptive power allocation.
