Towards subjective quality assessment of point cloud imaging in augmented reality

Applications of Digital Image Processing XLI

Pinheiro

Duarte

et al. 2018

Self Cite

Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. Likewise, the correlation with a subjective test using a different representation of the point cloud data also showed poor correlation. These results suggest the need for more reliable objective quality metrics and further studies on adequate point cloud data representations.

Section: Related Workmentioning

confidence: 99%

Point cloud subjective evaluation methodology based on reconstructed surfaces

Applications of Digital Image Processing XLI

Pinheiro

Duarte

et al. 2018

Self Cite

“…In, 15,16 we proposed an interactive subjective evaluation of point cloud geometry using a typical desktop setup and a head-mounted display in an augmented reality scenario, respectively. In both cases, two different types of distortions were evaluated, namely Gaussian noise and octree-pruning, to simulate position errors from sensor inaccuracies and compression-like artifacts, correspondingly.…”

Section: Javaheri Et Almentioning

confidence: 99%

“…A double-stimulus impairement scale (DSIS) methodology has already been proposed under two different experimental frameworks; that is, a typical desktop setup and a head-mounted display in an augmented reality scenario. 15,16 Although DSIS is preferred for its high accuracy and reliability in constructing a scale of perceptual references, it does not necessarily correspond to a realistic application where no reference of the distorted object is provided. In particular, it is substantially different when both the reference and the processed contents are visualized simultaneously, since the subjects typically rate based on relative geometrical differences that can be easily observed.…”

Section: Introductionmentioning

confidence: 99%

On the performance of metrics to predict quality in point cloud representations

Applications of Digital Image Processing XL

Ebrahimi

2017

Self Cite

Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

“…Alexiou et al 3,4 performed subjective evaluation of geometry-only contents subject to Gaussian noise and octreebased compression. The distorted models were visualized as raw point clouds and assessed using an interactive protocol in a desktop setup and an augmented reality scenario, respectively.…”

Section: Introductionmentioning

confidence: 99%

A novel methodology for quality assessment of voxelized point clouds

Torlig

Applications of Digital Image Processing XLI

Fonseca

et al. 2018

Self Cite

Recent trends in multimedia technologies indicate a significant growth of interest for new imaging modalities that aim to provide immersive experiences by increasing the engagement of the user with the content. Among other solutions, point clouds denote an alternative 3D content representation that allows visualization of static or dynamic scenes in a more immersive way. As in many imaging applications, the visual quality of a point cloud content is of crucial importance, as it directly affects the user experience. Despite the recent efforts from the scientific community, subjective and objective quality assessment for this type of visual data representation remains an open problem. In this paper, we propose a new, alternative framework for quality assessment of point clouds. In particular, we develop a rendering software, which performs real-time voxelization and projection of the 3D point clouds onto 2D planes, while allowing interaction between the user and the projected views. These projected images are then employed by two-dimensional objective quality metrics, in order to predict the perceptual quality of the displayed stimuli. Benchmarking results, using subjective ratings that were obtained through experiments in two test laboratories, show that our framework provides high predictive power and outperforms the state of the art in objective quality assessment of point cloud imaging.