The ability to provide a realistic perception of depth is the core added functionality of modern 3D video display systems. At present, there is no standard method to assess the perception of depth in 3D video. Existence of such methods would immensely enhance the progression of 3D video research. This paper focuses on the depth perception assessment in color plus depth representation of 3D video. In this paper, we subjectively evaluate the depth perceived by the users on an auto stereoscopic display, and analyze its variation with the impairments introduced during the compression of the depth images. The variation of the subjective perception of depth is explained based on another evaluation that is carried out to identify the Just Noticeable Difference in Depth (JNDD) perceived by the subjects. The JNDD corresponds to the sensitivity of the observers to the changes in depth in a 3D video scene. Even though only the effects of compression artifacts are considered in this paper, the proposed assessment technique, based on the JNDD values can be used in any future depth perception assessment work.

Three different advanced adaptation techniques for improving the video perception of users are proposed in this paper. The proposed techniques exploit different adaptation decision-taking and adaptation approaches to adapt particular core parameters while considering diverse contextual information and constraints to achieve improved video perception of users. The first proposed technique employs a utility-based adaptation approach to perform adaptation operations on spatial resolution, frame rate, and quality scalability parameters according to the content-related contextual information (i.e., motion activity and structural feature) while fulfilling network bandwidth and terminal display size constraints. Using this technique, video contents can be adapted with the scalability parameters best fitting users' and contextual constraints' needs to achieve improved video perception. The second technique relies on prioritizing key frame, non-key frame, and temporal layer parameter-related network abstraction layer units to adapt video contents to satisfy network bandwidth constraint. The rate-distortion performances of adapted video contents can be improved by utilizing this technique in adaptation operations both in terms of bit rate of adapted video contents and video perception of users. The third technique is based on adapting the bit rate of 3-D video contents according to the changes in ambient illumination of the viewing environment. The adaptation results evaluated by either subjective or objective quality assessment techniques prove that all of the proposed techniques are efficient to improve the video perception of users.

Many researchers have been developing objective video quality assessment methods due to increasing demand for perceived video quality measurement results by end users to speed-up advancements of multimedia services. However, most of these methods are either Full-Reference (FR) metrics, which require the original video or Reduced-Reference (RR) metrics, which need some features extracted from the original video. No-Reference (NR) metrics, on the other hand, do not require any information about the original video; hence, are much more suitable for applications like video streaming. This paper presents a novel, objective, NR video quality assessment algorithm. The proposed algorithm is based on utilization of spatial extent of video, temporal extent of video using motion vectors, bit rate, and packet loss ratio. Test results obtained using LIVE video quality database demonstrate the accuracy and robustness of the proposed metric.

Despite the burgeoning advances in 3D video technologies, 3D video adaptation is still in its infancy. 3D video has a multidimensional nature. To support the growth of 3D adaptation technologies, key factors that characterise this nature should be thoroughly studied. Spatial resolution is one of the important factors characterising this nature. Thus, particularly the effect of different spatial resolutions of depth maps on video quality and depth perception should be investigated. Similarly, the influence of the content related factors on the perception of different spatial resolutions of the depth maps should also be investigated. Therefore, evaluation studies are conducted using spatially scaled depth maps encoded at different bit rates and their original colour texture counterparts. The depth maps utilised in the evaluations present different spatial and motion complexity characteristics. The results demonstrate that when the spatial resolutions of the depth maps increase, both the video quality and depth perception improve. The amount of improvement is highly correlated with the structural and motion complexity characteristics of the depth maps.

To speed-up the proliferation of advanced 3-Dimensional (3D) technologies into the consumer market, the influence of these technologies on the perception of 3D video should be determined. Currently, this can only be achieved using either subjective assessment techniques or 2D objective quality evaluation models. Even though the subjective assessment techniques are better than the objective models from the accuracy point of view, they are time consuming and costly. Thus, 2D objective quality evaluation models correlating with Human Visual System (HVS) should be used to predict the 3D video quality perception of users in a reliable way with less effort. Video Quality Metric (VQM), which is a standardized 2D objective quality measurement model due to its well correlation with HVS, is used to predict 3D video quality perception of users reliably. However, ambient illumination context of the viewing environment, which has an effect on 3D video quality perception, is not considered in the quality assessments by VQM. Content adaptation is one of the key applications that need to use the perceived 3D quality assessments under different ambient illumination conditions at regular basis for ensuring improved video experience of users. Therefore, the standardized VQM model is extended using ambient illumination context and content related contexts (i.e., motion, structural feature, and luminance contrast) to predict 3D video quality measurement under a particular ambient illumination condition. The results prove that the extended VQM model can be efficiently utilized to predict the video quality perception of 3D video under a particular ambient illumination condition.