arXiv subjects

Aljosa Smolic

Publications and source records attributed to Aljosa Smolic.

39 records · Page 3Linked to original sources

arXiv2017

Estimation of optimal encoding ladders for tiled 360° VR video in adaptive streaming systems

Given the significant industrial growth of demand for virtual reality (VR), 360° video streaming is one of the most important VR applications that require cost-optimal solutions to achieve widespread proliferation of VR technology. Because of its inherent variability of data-intensive content types and its tiled-based encoding and streaming, 360° video requires new encoding ladders in adaptive streaming systems to achieve cost-optimal and immersive streaming experiences. In this context, this paper targets both the provider's and client's perspectives and introduces a new content-aware encoding ladder estimation method for tiled 360° VR video in adaptive streaming systems. The proposed method first categories a given 360° video using its features of encoding complexity and estimates the visual distortion and resource cost of each bitrate level based on the proposed distortion and resource cost models. An optimal encoding ladder is then formed using the proposed integer linear programming (ILP) algorithm by considering practical constraints. Experimental results of the proposed method are compared with the recommended encoding ladders of professional streaming service providers. Evaluations show that the proposed encoding ladders deliver better results compared to the recommended encoding ladders in terms of objective quality for 360° video, providing optimal encoding ladders using a set of service provider's constraint parameters.

cs.MM↗

arXiv2017

Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS

Many modern video processing pipelines rely on edge-aware (EA) filtering methods. However, recent high-quality methods are challenging to run in real-time on embedded hardware due to their computational load. To this end, we propose an area-efficient and real-time capable hardware implementation of a high quality EA method. In particular, we focus on the recently proposed permeability filter (PF) that delivers promising quality and performance in the domains of HDR tone mapping, disparity and optical flow estimation. We present an efficient hardware accelerator that implements a tiled variant of the PF with low on-chip memory requirements and a significantly reduced external memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded GPUs when scaled to the same technology node). The low area and bandwidth requirements make the accelerator highly suitable for integration into SoCs where silicon area budget is constrained and external memory is typically a heavily contended resource.

eess.IV↗

arXiv2017

Viewport-aware adaptive 360° video streaming using tiles for virtual reality

360° video is attracting an increasing amount of attention in the context of Virtual Reality (VR). Owing to its very high-resolution requirements, existing professional streaming services for 360° video suffer from severe drawbacks. This paper introduces a novel end-to-end streaming system from encoding to displaying, to transmit 8K resolution 360° video and to provide an enhanced VR experience using Head Mounted Displays (HMDs). The main contributions of the proposed system are about tiling, integration of the MPEG-Dynamic Adaptive Streaming over HTTP (DASH) standard, and viewport-aware bitrate level selection. Tiling and adaptive streaming enable the proposed system to deliver very high-resolution 360° video at good visual quality. Further, the proposed viewport-aware bitrate assignment selects an optimum DASH representation for each tile in a viewport-aware manner. The quality performance of the proposed system is verified in simulations with varying network bandwidth using realistic view trajectories recorded from user experiments. Our results show that the proposed streaming system compares favorably compared to existing methods in terms of PSNR and SSIM inside the viewport.

cs.MM↗