## 3 Methods
### Proposed No-Reference Depth Map Quality Evaluation Model  
#### 3.1.1 Depth Map Edge Confidence Measure  
Conventional no-reference quality evaluation methods assess the quality of the rendered colour views primarily when DIBR is used. Contrary to this, the approach adopted in our research focuses on evaluating the quality of the depth maps used in the rendering process in scenarios where there is no real reference to evaluate their quality for immersive video applications. Our proposed model targets building a measure to quantify the edge confidence in the depth maps used in rendering the views through DIBR. To achieve this, we have conducted several experiments where we introduced errors through varying levels of compression into the depth maps used in the rendering process. As a result of this initial experimentation, a difference map comparing the various compressed depth maps with the original one provided a good indication of the errors introduced into the depth maps. It was then observed that the highest level of error in the compressed depth maps was concentrated around the edges within a depth map, which motivated for devising a depth map edge confidence measure. Figure 3 presents the difference maps obtained from comparing the depth maps, compressed at quality parameter (QP) levels of 22, 32 and 42, with the original depth map for the _Musicians_ colour sequence. These QPs broadly represent high quality (low compression), medium quality (medium compression) and low quality (high compression) levels, respectively. The errors in the difference maps appear at varying luminance levels. The brighter the luminance values, the larger the difference for each pixel between the compared depth maps (i.e., the higher the error occurring, due to compression) at the corresponding locations. It can be noticed that the errors increase with the higher compression levels. The brighter luminance values at all compression levels occur on the edges in the depth maps, which represent notable changes in-depth values (i.e., different depth planes) rather than edges of objects within a colour view. The errors in these areas have a significant impact on the quality of the  
Figure 2: Main blocks of the proposed no-reference depth map quality evaluation model. overall view rendered utilising the particular depth map. This observation reinforces the depth map edge confidence approach, which is a clear indication of the quality of the depth map. The results in Figure 3 have been obtained via comparison between a selected depth map and its compressed versions. This scenario is useful for computing the depth map edge confidence for the compressed versions, but this cannot be the case if the edge confidence is required for the original depth map, where reference depth map is not available. To compute the edge confidence for a stand-alone depth map without a reference, an edge detection process is applied for both the depth map and its associated colour view, which results in building an edge confidence map. The per-pixel edge confidence map outlines the significant edge information contained in both the depth map and corresponding colour view. Subsequent to the edge detection process (based on the Sobel operator) applied on both the depth map and colour video component, the edge confidence map is constructed. The detected edge information is classified under three groups by different intensity values in the resulting confidence map. The first group comprises the pixels that are classified as edges in both the colour view and depth map. The second group consists of the pixels that are classified as edges only in the colour view and the third group consists of the pixels that are classified as edge pixels only in the depth map. The rest of the pixels are not classified as an edge in either the depth map or the associated colour view. Figure 4 presents examples of the resulting edge confidence maps for a selection of depth maps utilised for rendering the _BMX_ colour sequence.