Abstract
Compared with traditional video surveillance systems, wireless video sensor systems are more suitable for emergency application scenarios, such as underground coal mine disaster rescue, due to their low power consumption and rapid deployment. Considering the limited computing power and transmission bandwidth of video sensor nodes, we propose an adaptive compression and hybrid multiple hypothesis based residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) to solve the high efficiency video coding (HEVC) problem in underground coal mines. First, a low-complexity adaptive sampling rate allocation method is performed on the encoding side. Second, by integrating the background subtraction idea, we combine the high-quality reconstruction of the foreground with the multi-hypothesis residual reconstruction of the background to improve the overall reconstruction effect of the video sequence. Simulation results show that the proposed algorithm can achieve higher reconstruction quality and efficiency than previous methods, especially in underground coal mine application scenarios.
Highlights
Because of the limited transmission bandwidth of wireless communication in underground coal mines, how to reconstruct high-quality video sequences from a limited number of samples has become the main research direction
Li et al [10] proposed a new motion estimation (ME) method called High Efficiency Video Coding Motion Estimation (HEVC-ME), which uses coding units of different sizes, the calculated residuals are subjected to Hardman transform first and sum the absolute values as the rate-distortion function to generate more accurate side information (SI)
RELATED WORK In this chapter, we briefly introduce the basic MH prediction and residual reconstruction framework and describe the research on adaptive sampling
Summary
Because of the limited transmission bandwidth of wireless communication in underground coal mines, how to reconstruct high-quality video sequences from a limited number of samples has become the main research direction. Y. Xu et al.: Adaptive DCVS Algorithm Based on NBC for Coal Mine Monitoring Video generates hypothesis blocks in the residual domain and calculates the linear prediction weights in measurement-domain. Li et al [10] proposed a new ME method called High Efficiency Video Coding Motion Estimation (HEVC-ME), which uses coding units of different sizes, the calculated residuals are subjected to Hardman transform first and sum the absolute values as the rate-distortion function to generate more accurate SI. With insight into the adaptive sampling strategy which can reduce background redundancy by well exploiting temporal correlation, an adaptive compression and hybrid multiple hypothesis residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) is presented. There are two contributions in the NBCAC-MHRR: 1) By using the normalized Bhattacharyya coefficient to shift the center of gravity of the video compression from frame to block, which provides convenience for adaptive compression and reconstruction. 2) A hybrid reconstruction framework that combines traditional reconstruction methods with multihypothesis-based residual reconstruction is used for the subsequent reconstruction, which improves the overall reconstruction performance
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