Objective

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In traditional video acquisition

a video signal is sampled based on Nyquist sampling theory with a sampling frequency greater than or equal to twice the maximum frequency of the signal. The spatial and temporal redundancy information in the video signal is removed by the conventional encoding method. As people's requirements on the quality of multimedia content are increasing

the burden on the video encoder is becoming heavier. However

the traditional video-coding method is unsuitable for the application environments with limits in power consumption

storage capacity

and computing power (e.g.

wireless video surveillance). Compressed sensing (CS) conducts sampling and compression simultaneously

thereby saving enormous sampling resources while reducing the sampling complexity significantly. Thus

this technique is suitable for application scenarios with a resource-deprived sampling side. CS-based distributed video coding attracts considerable attention

in which utilizing the correlation among frames to reconstruct video efficiently has become a main research area. Multi-hypothesis (MH) prediction is a key technique in predicting residual reconstruction algorithm for compressed video sensing. In the existing MH prediction algorithm

the block size usually remains unchanged during the prediction process. The scheme accuracy depends on the similarity between the hypothetical and current blocks; hence

high similarity of the block group is assumed to lead to a good prediction result. Nevertheless

the content motion type is complicated for some image blocks in a video frame. The invariable-size block prediction scheme consequently leads to inconsiderably similar matching blocks and poor prediction results. Simulations indicate that the motion vectors of the image block in the motion gradual region are close

and therefore

searching the best match for each single block produces an unnecessary computing burden. The existing MH prediction algorithm generally has two disadvantages. First

the prediction accuracy for video frames with complex movement is poor. Second

for the smooth motion region or frames

the motion vectors of adjacent image blocks are highly similar

and searching the best matching block for each one separately leads to high algorithm complexity.

Method

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For these problems

we propose a hierarchical MH prediction method (Hi-MH) that adopts different block-matching prediction methods for regions with different motion complexities and then introduce an implementation method. For the image block in smooth motion regions

the motion vector of the current block is predicted by that of the neighboring image block to decrease the motion estimation complexity (Motion estimation starts from a large block with a size four times of the observing block

and the motion estimation process from large block to small block is controlled by a suitable threshold to ensure the accuracy of each motion estimation until the block size is smaller than the observing block size

which means that this image block does not belong to a flat motion area).For the image blocks with complex movement

smaller blocks are used to find the best match and then adopt the MH prediction in pixel domain to obtain the prediction block. For the image blocks with a considerably complex movement

the autoregressive model is used to predict every individual pixel in the blocks. The reconstruction superiority of the regression model improves the prediction accuracy.

Result

2

A comparison of the result of Hi-MH and that of an MH prediction scheme based on fast diamond search with two matching regions (MH-DS) shows that the prediction time for each frame decreases by 1.43 s and 1.73 s for the Foreman and Coastguard sequences

respectively. The reconstruction accuracy of Hi-MH is higher than those of 2sMHR (Gw_2sMHR

Fw_2sMHR) and MH-DS. At the sample rate from 0.1 to 0.5 for non-key frames

the average PSNR of Hi-MH is 1.3 dB better than that of Fw_2sMHR

1.1 dB better than that of Gw_2sMHR

and 0.34 dB better than that of MH-DS. Compared with the PBCR algorithm which currently has the best reconstruction accuracy

the Hi-MH improves the reconstruction accuracy by 1 dB for some complex motion sequences.

Conclusion

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1) The Hi-MH algorithm is improved based on the MH-DS algorithm. For some image blocks with complex motion

the hierarchical motion estimation scheme in Hi-MH can find more accurate matching regions and obtain high-quality hypothesis block groups to improve the prediction accuracy of those blocks. The block classification prediction scheme in Hi-MH improves the prediction accuracy for some severely deformed image blocks; therefore

the overall reconstruction quality is enhanced. 2) For fast-moving video sequences

the Hi-MH algorithm has a significant improvement in reconstruction result over the PBCR-DCVS algorithm which currently has the best reconstruction quality. Local correlation in the videos is fully utilized because the Hi-MH algorithm proposed in this study can obtain higher accuracy image block-matching regions through the fast diamond search method and hierarchical motion estimation. Thus

the video reconstruction result is better. For slow-moving video sequences

such as Mother-daughter and Coastguard

the Hi-MH algorithm remains superior to the PBCR-DCVS algorithm at low sampling rates. As the sampling rate increases

the advantage gradually disappears. The reason is that at low sampling rates

the PBCR-DCVS algorithm cannot find more high-quality hypothetical block groups but Hi-MH can better solve this problem

thereby greatly improving the reconstruction quality. As the sampling rate increases

numerous observations are transmitted to the decoder

and PBCR-DCVS can find a good matching block group that helps in high-quality reconstruction. However

the neighborhood motion vector prediction technique used in Hi-MH to reduce the motion estimation complexity decreases the quality of the matching block group and the reconstruction quality. In general

the Hi-MH algorithm reduces the computational complexity for video sequences or regions with simple movement and improves the prediction accuracy for video sequences or regions with complex motion patterns.