Reduced-complexity multiview prediction scheme with content-adaptive disparity vector estimation

Abstract

Disparity estimation is a highly complex and time consuming process in multiview video encoders. Since multiple views taken from a two-dimensional camera array need to be coded at every time instance, the complexity of the encoder plays an important role besides its rate-distortion performance. In previous papers we have introduced a new frame type called the D (derived) frame that exploits the strong geometrical correspondence between views, thereby reducing the complexity of the encoder. By employing the D frames instead of some of the P frames in the prediction structure, significant complexity gain can be achieved if the threshold value, which is a keystone element to adjust the complexity at the cost of quality and/or bit-rate, is selected wisely. A new adaptive method to calculate the threshold value automatically from existing information during the encoding process is presented. In this method, the threshold values are generated for each block of each D frame to increase the accuracy. The algorithm is applied to several image sets and 20.6% complexity gain is achieved using the automatically generated threshold values without compromising quality or bit-rate.