H.264/SVC Rate Allocation for Spatial Scalability Based on Perceptual Quality Metric

Abstract

H.264 scalable extension (SVC), which is constructed based on H.264/AVC, is the most recent scalable video coding standard. It offers three scalabilities in spatial, temporal, and quality, to meet multiple requirements simultaneously. Spatial scalability that can support multiple display resolutions with a wide range of bitrates is used widely. How to efficiently allocate a given total bitrate among multiple layers under the bandwidth constraint is an important issue and should be solved at first.

The base layer is usually treated more important than the enhancement layer because the information in base layer will often be re-used in enhancement layers. Therefore, under a bandwidth constraint, we usually run SVC by fixing the Qutization Parameter (QP) or the bitrate of the base layer while adaptively adjusting the ones of the enhancement layers. However, it is observed that Human Visual System (HVS) is more sensitive to higher resolution videos; in other words, the quality degradation at higher layers to human eyes would be more serious than that at lower layers. The main objective of this work is to achieve best and equal quality for each resolution layer under a given bandwidth constraint.

This thesis proposes a rate allocation method for SVC spatial scalability based on perceptual quality metric. We utilize the subjective metric, instead of conventional objective measurement PSNR, to measure video quality. Each resolution layer is measured by the quality metric and allocated with the corresponding rate to have similar quality. The disadvantage of the conventional fixed QP scheme that the higher resolution layer has worse subjective quality is improved. In simulations, several video sequences with various total rate constraints are experimented. The proposed method can efficiently allocate the rate for each layer with almost the same video quality in subjective measurement.