Rate control for non-real-time video encoding

1998 ◽  
Author(s):  
IMing Pao ◽  
Ming-Ting Sun
2012 ◽  
Vol 195-196 ◽  
pp. 998-1002
Author(s):  
Xiao Ping Huang ◽  
Ru Jun Cao ◽  
Peng Ying Wang

The skipping frame algorithm in TMN8 rate control only depending on buffer state regardless of image characteristic, may skip important frames with large motion and, as a result, video quality seriously reduced. An adaptive skipping frame algorithm is proposed for low-bit rate real-time video encoding. The occurrence of frame skipping is jointly dependent on the temporal and spatial contents of the video, and achieves a balanced spatial and temporal quality to enhance the overall perceptual quality. Experimental results show that the proposed algorithm can achieve the better subjective and objective video quality than TMN8s algorithm, without introducing any computation complexity.


Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.


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