A regression-based framework for estimating the objective quality of HEVC coding units and video frames

• A no-reference objective quality estimation framework is proposed. The framework is suitable for any block-based video codec.
• In the proposed solution, features are extracted from coding units and summarized to form features at frame levels. Stepwise regression is used to select the important feature variables and reduce the dimensionality of feature vectors.
• Thereafter, a polynomial regression-based approach is used to model the nonlinear relationship between the feature vectors and the true objective quality values. Such values are estimated for coding units and video frames.
• The proposed framework is implemented using MPEG-2 and HEVC. The objective quality estimation results are compared against an existing state-of-the-art solution and quantified using the Pearson correlation factor and the root mean square error measure.

A no-reference objective quality estimation framework is proposed. The framework is suitable for any block-based video codec. In the proposed solution, features are extracted from coding units and summarized to form features at frame levels. Stepwise regression is used to select the important feature variables and reduce the dimensionality of feature vectors. Thereafter, a polynomial regression-based approach is used to model the nonlinear relationship between the feature vectors and the true objective quality values. Such values are estimated for coding units and video frames. The proposed framework is implemented using MPEG-2 and HEVC. The objective quality estimation results are compared against an existing state-of-the-art solution and quantified using the Pearson correlation factor and the root mean square error measure.