Disparity map quality evaluations on Middlebury benchmark and real-world outdoor scenery show that the proposed implementation is able to generate high quality disparity maps.
This paper proposes a hardware-oriented adaptive window size disparity estimation (AWDE) algorithm and its real-time reconfigurable hardware implementation that targets HR video with high quality disparity results.
Manual in-painting for reference disparity generation: Most of the approaches for disparity ground truth calculation contain a step of smoothing or filling small wholes or noise-based artifacts to achieve a better quality disparity as the reference.
Unfortunately, computing disparity maps is expensive and difficult and most leading assessment models are based on features drawn from the outputs of high complexity disparity calculation algorithms that deliver high quality disparity maps.
More, recent studies have emphasized the use of high complexity dense stereo matching algorithms that deliver high quality disparity maps, such as the matching algorithm [17] used in [7], dynamic programming [15, 18], the Depth Estimation Reference Software [19] used in [12], and combinations of sparse and dense disparity estimation methods [16].
Since the histograms of perceptually processed low quality disparity maps extracted by low complexity stereogram algorithms such as DSSIM or DSAD do not result in very good fitting results, then the average GGD fitting error is extracted as a useful feature: begin{array}rcl@ varepsilon = frac{1}{N}sumlimits_{x} {left| {H x) - {f_{x}}(x)} right|} end{array} (14).
In this paper, we propose a stereo matching algorithm based on distance transform to generate high-quality disparity maps with occlusion handling.
Two well-known global stereo matching approaches, belief propagation [18] and graph cut [19], can produce high-quality disparity maps but require very high computational complexity.
Therefore, how to generate the high-quality disparity map with low computing time becomes an important task in 3D vision systems.
If the high-quality disparity map could be produced by complex algorithms [17, 18], the computation time is usually extremely high.
