Abstract
The work in this paper examines the effects of group of pictures on H.264 multiview video coding bitstream over an erroneous network with different error rates. The study considers analyzing the bitrate performance for different GOP and error rates to see the effects on the quality of the reconstructed multiview video. However, by analyzing the multiview video content it is possible to identify an optimum GOP size depending on the type of application used. In a comparison test, the H.264 data partitioning and the multi-layer data partitioning technique with different error rates and GOP are evaluated in terms of quality perception. The results of the simulation confirm that Multi-layer data partitioning technique shows a better performance at higher error rates with different GOP. Further experiments in this work have shown the effects of GOP in terms of visual quality and bitrate for different multiview video sequences.
Highlights
Human vision system (HVS) has stimulated an interest in acquisition of visual content and provided an acceptable guide to its understanding
In the analysis leading to the determination of disparity map, correspondence problem still remain a major source of error
To reduce the error contribution due to the aforementioned, comparison of quantitative results with Normalized Cross Correlation (NCC) method, Daisy descriptor, Error Quadratic Means (EQM), and Local evidence have been employed in most research work related to depth map
Summary
Human vision system (HVS) has stimulated an interest in acquisition of visual content and provided an acceptable guide to its understanding. The mathematical description of a remarkable multi-camera topology: the trapezoidal camera architecture (TCA), for acquisition of visual content is proposed. This is in spite of the fact that best observability of the object surface with a single ring camera arrangement can be achieved when the ring is at mid-height of the target object [3]. With respect to depth map acquisition, optimization of matching energy function defined by Markov Random Field (MRF) can be optimized This efficiently provides for rectification of the image pairs acquired through convergence camera array [20].
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