Efficient multiview depth representation based on image segmentation

The persistent improvements witnessed in multimedia production have considerably augmented users demand for immersive 3D systems. Expedient implementation of this technology however, entails the need for significant reduction in the amount of information required for representation. Depth image-based rendering algorithms have considerably reduced the number of images necessary for 3D scene reconstruction, nevertheless the compression of depth maps still poses several challenges due to the peculiar nature of the data. To this end, this paper proposes a novel depth representation methodology that exploits the intrinsic correlation present between colour intensity and depth images of a natural scene. A segmentation-based approach is implemented which decreases the amount of information necessary for transmission by a factor of 24 with respect to conventional JPEG algorithms whilst maintaining a quasi identical reconstruction quality of the 3D views.peer-reviewe

Performance improvement of segmentation-based depth representation in 3D imagery by region merging

The feasible implementation of immersive 3D video systems entails the need for a substantial reduction in the amount of image information necessary for representation. Multiview image rendering algorithms based on depth data have radically reduced the number of images required to reconstruct a 3D scene. Nonetheless, the compression of depth maps still poses several challenges due to the particular nature and characteristics of the data. To this end, this paper outlines a depth representation technique, developed in our earlier work, that exploits the correlation intrinsically present between color intensity and depth images capturing a natural scene. In this technique, a segmentation-based algorithm that is backwards compatible with conventional video coding systems is implemented. The effectiveness of our previous technique is enhanced in this contribution by a region merging process on the segmented regions, which results in a decrease in the amount of information necessary for transmission or storage of multiview image data by a factor of 20.5 with respect to the reference H.264/AVC coding methodology. This is furthermore achieved whilst maintaining a 3D image reconstruction and viewing quality which is quasi identical to the referenced approach.peer-reviewe

Metrics for Stereoscopic Image Compression

Metrics for automatically predicting the compression settings for stereoscopic images, to minimize file size, while still maintaining an acceptable level of image quality are investigated. This research evaluates whether symmetric or asymmetric compression produces a better quality of stereoscopic image. Initially, how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly compressed stereoscopic image pairs was investigated. Two trials with human subjects, following the ITU-R BT.500-11 Double Stimulus Continuous Quality Scale (DSCQS) were undertaken to measure the quality of symmetric and asymmetric stereoscopic image compression. Computational models of the Human Visual System (HVS) were then investigated and a new stereoscopic image quality metric designed and implemented. The metric point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes in these regions. The PSNR results show that symmetric, as opposed to asymmetric stereo image compression, produces significantly better results. The human factors trial suggested that in general, symmetric compression of stereoscopic images should be used. The new metric, Stereo Band Limited Contrast, has been demonstrated as a better predictor of human image quality preference than PSNR and can be used to predict a perceptual threshold level for stereoscopic image compression. The threshold is the maximum compression that can be applied without the perceived image quality being altered. Overall, it is concluded that, symmetric, as opposed to asymmetric stereo image encoding, should be used for stereoscopic image compression. As PSNR measures of image quality are correctly criticized for correlating poorly with perceived visual quality, the new HVS based metric was developed. This metric produces a useful threshold to provide a practical starting point to decide the level of compression to use