Performing perspective transformation from side view to central-view frames will result in an outlier, miss transformed area, as shown in Fig. 5(a). The perspectively transformed images, and , and the reconstructed central-view images, , are used to perform block matching to estimate disparity and MVs, denoted as and , respectively. The SIF of a central-view image not transmitted can be reconstructed through weighted motion compensated prediction by above and , in which the latter were estimated from . This BMP process would reconstruct the SIF, , shown in Fig. 5(b), where is the block in , and are the disparity and MVs estimated between reconstructed interview images, e.g., and , and between , respectively. The COMPETE flowchart is shown in Fig. 6. One is partitioned into blocks, , and a large block consists of blocks, i.e., , in which is the current block, i.e., . The four block MVs in , , are obtained by performing motion estimation (ME) between and for the co-located . If , it means in is a no-motion block and can be reconstructed by direct copy from its previous image, i.e., . If , then is a motion block and the corresponding disparity block in side-view transformed images, and , and ’s MVs are combined with weights proportional to block fidelity to yield a more accurate compensated block for the in . We take the ME process for a by referencing left- and central-view images as an example and the right-view one can be carried out in the same way. The first-phase block disparity estimation is performed between and , denoted as , which will yield the best matched block from with a . If the best matched block does not reside on the outlier of , the second-phase block ME is performed, in which the search range in is two blocks wide along vertical and horizontal directions and centered at the co-located coordinate of on with the offset . It yields one , and the second can be obtained by the same procedure . The other two MVs, and , are estimated from the right-view video through the same procedure. When performing MC for an , if any image block reached through the inner-path MV, , resides on the outlier, then its is set zero. Let denote the image block obtained from the co-located block on an with its MV, , and the reconstruction for the SIF, , can be represented as Display Formula
(1)where the first term yields the weighted central-view image by utilizing MVs of and the second one from . In general, the should be proportional to the normalized fidelity of the corresponding best matched block with respect to the co-located blocks in central view. The for one MC block reached through can be computed as Display Formula
(2)in which denotes the sum of absolute distortion whose reciprocal can be used as block fidelity. On the other hand, if all matched blocks reside on the outlier, there would be no prediction result that can satisfy the assumed scenario. Under this condition, only the reference MV, , estimated between the two reconstructed central-view images, and , can be used to predict the SIF. The bidirectional MC is used to reconstruct the block of the SIF: Display Formula
(3)To further yield the optimal weight for a MC block , the linear minimum mean squared error (LMMSE) estimator can be adopted. How to compute the LMMSE weights, , is described in the 1. Experiments showed that adopting LMMSE weights can improve the SIF PSNR up to 0.1 and 0.3 to 0.4 dB for low and medium-to-high complexity videos, respectively, compared to those adopting weights proportional to block fidelity presented by Eq. (2).