Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications

Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications

Video Quality Assessment: From 2D to 3D - Challenges and Future Trends

International audienceThree-dimensional (3D) video is gaining a strong momentum both in the cinema and broadcasting industries as it is seen as a technology that will extensively enhance the user's visual experience. One of the major concerns for the wide adoption of such technology is the ability to provide sufficient visual quality, especially if 3D video is to be transmitted over a limited bandwidth for home viewing (i.e. 3DTV). Means to measure perceptual video quality in an accurate and practical way is therefore of highest importance for content providers, service providers, and display manufacturers. This paper discusses recent advances in video quality assessment and the challenges foreseen for 3D video. Both subjective and objective aspects are examined. An outline of ongoing efforts in standards-related bodies is also provided

Analysis of binaural cue matching using ambisonics to binaural decoding techniques

Last year Google enabled spatial audio in head-tracked 360 videos using Ambisonics to binaural decoding on Android mobile devices. There was some early criticism of the 1st order to binaural conversion employed by Google, in terms of the quality of localisation and noticeable frequency response colouration. In this paper, the algorithm used by Google is discussed and the Ambisonics to Binaural conversion using virtual speakers analysed with respect to the resulting inter-aural time, level, and spectrum differences compared to an example HRTF data set. 1st to 35th order Ambisonics using multiple virtual speaker arrays are implemented and analysed with inverse filtering techniques for smoothing the frequency spectrum also discussed demonstrating 8th order decoding correctly reproducing binaural cues up to 4 kHz.N/

Depth Estimation for 2D-to-3D Image Conversion Using Scene Feature

In this modern era 3D supportive hardware popularity is increased but the demand for 3D contents and there availability is not matching. They are still dominated by its 2D counterpart hence there is need of 3D contents. While doing 2D-to-3D image or video conversion depth estimation is a key step and a bit challenging procedure. There are distinct parameters that can be considered during conversion like, structure from motion, defocus, perspective geometry, etc. Until now many researchers have been proposed different methods to close this gap by considering one or many parameters. In this paper for depth estimation, conversion using scene feature is used. Here color is chosen as a scene feature. Intensity information is used here to estimate depth image, hence RGB to HSV conversion is performed from which Value (V) deals with intensity information. RGB to HSV conversion is implemented on FPGA. The proposed method is prototyped on Spartan 3E FPGA based developing board and MATLAB. DOI: 10.17762/ijritcc2321-8169.15068