Region-of-interest based conversational HEVC coding with hierarchical perception model of face

In this paper, we propose a region-of-interest (ROI) based HEVC coding approach for conversational videos, with a novel hierarchical perception model of face (HP model), to improve the perceived visual quality of state-of-the-art HEVC standard. In contrast to the previous ROI-based video coding approaches, this novel HP model allows the unequal importance of facial features (e.g., the eyes and mouth) within the facial region, by generating a pixel-wise weight map. Benefitting from such a perception model, the adaptive coding tree unit (CTU) partition structure is developed to alleviate the encoding complexity of HEVC, without any degradation of the visual quality in facial regions, especially in the regions of facial features. Subsequently, for the rate control in HEVC a weight-based unified rate-quantization (URQ) scheme, instead of the conventional pixel-based URQ scheme, is proposed to adaptively adjust the value of quantization parameter (QP). Such an adaptive adjustment of QPs is capable of allocating more bits to the face/facial features with respect to our HP model, and as a result, the visual quality of face, in particular facial features, can be enhanced for conversational HEVC coding. Finally, the experimental results show that the perceived visual quality of our approach is greatly improved, with even less encoding time, for conversational video coding on the HEVC platform.