4.6  Conclusions


In this chapter, we have proposed two novel image rendering algorithms.

The first algorithm is based on a variant of the relief texture method. As opposed to the original approach adapted to a computer-graphics framework, the proposed algorithm directly integrates the internal and external camera parameters. To enable accurate rendering, the proposed rendering algorithm decomposes the standard image warping equation into a sequence of two simpler 2D operations. The first 2D operation is further decomposed into two 1D operations, thereby enabling a simple re-sampling of pixels along rows and columns. The second 2D operation is an homography transform that can be accurately implemented and efficiently executed using a standard GPU. As a result, the key features of the algorithm are that it avoids holes in the synthetic image and it fits well to a GPU architecture.

The second algorithm is based on an inverse mapping method that scans the destination image and calculates the position of the corresponding pixels in the source image. Therefore, this methods allows a simple re-sampling of synthetic pixels because it prevents rounding the coordinates of synthetic pixels to the underlying pixel grid. The proposed rendering algorithm features a simple and accurate re-sampling of synthetic pixels and easily enables the combination of multiple source images such that occluded regions can be correctly handled. Therefore, this algorithm can potentially offer a higher rendering quality than the relief texture mapping.

Next, to address the problem of determining the visibility of background and foreground pixels in the rendered image, we have discussed the occlusion-compatible scanning order. This concept was adopted from literature and validated within our framework. Here, we have derived that a proper scanning can be made for rectified and unrectified views.

Additionally, two occlusion-handling techniques were introduced. The first technique is based on the assumption that occluded regions correspond to background pixels. Occluded pixels are therefore padded using the color of neighboring background pixels. Although the presented techniques are based on heuristic rules, experimental results have shown that the relief texture combined with the proposed occlusion-handling methods yield between 1.9 dB and 3.8 dB rendering-quality improvement. The second occlusion-handling method combines two source images that cover all regions of the video scene for synthesizing a single virtual view. Note that the proposed inverse mapping algorithm integrates and combines the two source images by careful pixel selection, so that occluded regions are properly handled. Experimental results indicate that such an occlusion-handling technique further improves the rendering quality by a range of 3.8 dB to 5.9 dB.

If there is one aspect of this chapter that comes clearly to the foreground, then it is a decent solution for the occlusion problem. If this is well managed, the rendering quality will be clearly improved, both objectively in terms of measured SNR (dB) and subjectively. It will become clear later in this thesis, that this also has a beneficial impact on the coding efficiency, since the visual data contains less disturbing and noisy pixels. A second aspect is that an efficient use of the multi-view geometry can significantly reduce the complexity of the rendering algorithms. Specifically, we have seen that the visibility of objects can be determined by solely using the multi-view geometry without any depth information. Additionally, the multi-view geometry enables an efficient and simple padding of background holes in the synthetic images. Finally, we have seen that a rendering equation relying on the multi-view geometry can be appropriately factorized, thereby enabling an efficient execution. The aforementioned advantages highlight that multi-view geometry constitutes a very powerful tool 1, which was indispensable in this chapter for making the improvements and optimizations with a limited number of assumptions and conditions.


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Figure 4.12 (a) Original view of the “Ballet” sequence captured by the camera 3. (b) Relief texture mapping algorithm.



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Figure 4.13 (a) Inverse-mapping algorithm. (b) Inverse-mapping algorithm using two source images.



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Figure 4.14 (a) Original view of the “Breakdancers” sequence captured by the camera 3. (b) Relief texture mapping algorithm.



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Figure 4.15 (a) Inverse-mapping algorithm. (b) Inverse-mapping algorithm using two source images.


1This remark is similar to a statement of Jean le Rond d’Alembert, a French mathematician, who stated that “Algebra is generous; she often gives more than is asked of her”