Depth estimation based on multiview matching with depth/color segmentation and memory efficient Belief Propagation

An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

“…Therefore, without loss of generality, in the following we extensively and systematically study the impact 5 For an original image represented with -bit luminance depth, and the corresponding encoded and decoded image , the PSNR is defined as 6 These values have been obtained for an absolute coding cost of view-0 AVC coding of 48 Kb/s, using a minimum QP equal to 32. of the estimated CSA on the observed MVC efficiency, and we assume the encoder settings to be fixed as in Table I. On the Akko&Kayo sequence, we also calculated the lowcomplexity cross-correlation based CSA estimator between the -th frames of reference view 0 and of the -th sequence view, namely .…”

“…For fair comparison of the coding results, the MVC coding cost and the multiple AVC coding cost have been compared under the constraint of equal average decoded sequence quality, measured in terms of Peak Signal-to-Noise Ratio 5 (PSNR), specifically, the , averaged on the 6 considered views, equals 32.24 dB with 1 dB standard deviation, whereas the , averaged on the 6 considered views, takes on the value of 32.59 dB with 0.34 dB standard deviation. 6 Therefore, the coding comparison is carried out on a fair, equal quality, basis.…”

“…To this aim, let us denote by the spatial width framed by the camera at the distance . We recognize that the width is (6) being the horizontal angular camera width. The horizontal size of the object equals to (7) Besides, we recognize that the vertical size of the object equals to (8) being the vertical angular camera width.…”

“…The availability of different views of the same scene enables multi-view oriented processing techniques, such as video scene summarization [3], moving object detection [4], face recognition [5], depth estimation [6], among others. Enhanced application-layer services that rely on these techniques can be envisaged, including multi-person tracking, biometric identification, ambience intelligence, and free-view point video monitoring.…”

Leveraging Multiview Video Coding in clustered Multimedia Sensor networks

“…When different sensors cameras acquire different views of the same scene, multi-view oriented processing techniques enable tasks such as video scene summarization [3], moving object detection [4], face recognition [5], depth estimation for 3D video rendering [6], surveillance, and social robotics to cite a few.…”