Efficient Error Frame Loss Recovery Model for Scalable Video Coding (SVC)

摘要

Video processing algorithms tend to improve over time in terms of image quality while increasing in its inter and intra dependency. Frames inter prediction exploits temporal similarities across a sequence of consecutive frames, while intra prediction exploits the macroblock's spatial similarities in the same frame. They both work together to efficiently compress the video stream (maximize the signal to noise ratio (SNR) while minimizing the used bandwidth (BW)). Thus, different parts of the video stream (blocks and/or frames) have different semantic importance, and thus require different degrees of protection against network losses to maintain a constant quality of service (QoS). This becomes even more important in layered codec (e.g., scalable video codec SVC/H.264), where the stream is compromised of more than one video layer. Based on the expected video experience, available bandwidth and compute resources, we could use one or more layers to achieve a certain level of experience. This becomes challenging in lossy networks, where losses could harm not only the immediate group of pictures (GOP), but will propagate across multi video layers. In this paper, we present a method to adequately distributed forward error correction (FEC) packets across multi layers to preserve the video experience under lossy conditions.