Non-Subsampled Contourlet Transform and Ground-Truth Score Generation Based Quality Assessment for DIBR-Synthesized Views

In recent years, there have been advancements in developing Depth-Image-Based Rendering (DIBR) views. However, the quality of these synthesized views is often degraded by inefficient in-painting techniques and synthesis procedures, leading to geometric and structural distortions. This paper introduces two novel approaches to evaluate the quality of DIBR synthesized views, using full reference (FR) and no-reference (NR) metrics. The proposed FR quality assessment (QA) metric is based on the observation that the deep features of the Non-Subsampled Contourlet Transform (NSCT) maps capture the perceptually important characteristics of the images. By calculating the difference between these deep feature vectors of the reference and distorted views, we determine the quality of the image. Moreover, a lot of existing NR metrics typically divide an image into blocks and assign the same subjective quality scores to each block for training a deep learning model. However, this approach is not suitable for DIBR synthesized views, as distortions are often localized in specific areas rather than affecting the entire view. Consequently, the performance of existing block-based deep-learning algorithms suffers due to the absence of accurate ground truth scores for each image block. To address this limitation, this work proposes an innovative method for calculating ground truth scores for individual image blocks. This process is similar to the proposed FR metric. Firstly, we obtain the deep features of NSCT map of an image block and the quality score for each block is calculated using its and the reference block's feature vector. These block-wise ground truth scores are used to train a deep learning model which serves as an NR metric for estimating the quality of a given test block. Finally, the predicted block-level quality values are aggregated to determine the overall quality of the entire image. Experimental results demonstrate that both the proposed algorithms perform better than the existing objective metrics for DIBR synthesized views.