Quantization-Distortion Models for Interlayer Predictions in H.264/SVC Spatial Scalability

Abstract

H.264 scalable extension (H.264/SVC) is the current state-of-the-art standard of the scalable video coding. Its interlayer prediction provides higher coding efficiency than previous standards. Since the standard was proposed, several attempts have been made to improve the performance based on its coding structure. Quantization-distortion (Q-D) modeling is a fundamental issue in video coding; therefore, this paper proposes new Q-D models for three interlayer predictions in 264/SVC spatial scalability, that is, interlayer motion prediction, intraprediction, and residual prediction. An existing single layer offline Q-D model is extended to H.264/SVC spatial scalable coding. In the proposed method, the residual power from the interlayer prediction is decomposed into the coding distortion and the prediction distortion. The prediction distortion is the mean square error (MSE) between two original signals that can be obtained by preprocessing with low complexity. Therefore, the coding distortion can be estimated based on both the quantization parameter (QP) and a precalculated prediction distortion before the encoding process. Consequently, the estimated quality based on the proposed models achieved a high accuracy of over 90% for the three interlayer predictions in average.