Scalable video coding (SVC) is a new video coding format which provides scalability in three-dimensional (spatio-temporal-SNR) space. In this paper, we focus on the adaptation in SNR dimension. Usually, an SVC bitstream may contain multiple spatial layers, and each spatial layer may be enhanced by several FGS layers. To meet a bitrate constraint, the fine-grained scalability (FGS) data of different spatial layers can be truncated in various manners. However, the contributions of FGS layers to the overall/collective video quality are different. In this work, we propose an optimized framework to control the SNR scalability across multiple spatial layers. Our proposed framework has the flexibility in allocating the resource (i.e., bitrate) among spatial layers, where the overall quality is defined as a function of all spatial layers' qualities and can be modified on the fly.
Medium grained scalability (MGS) of scalable video coding is expected to be of high interest due to the advantage of packetbased scalability. In this paper, we study multilayer adaptation for MGS-based bitstream. Our adaptation method not only considers which packets to be dropped, but also modifies layer dependency of NAL units to maintain bitstream conformance. Compared to conventional method of SVC, the proposed method can both expand the range of supported bitrates and improve the quality.
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