A Gradient Intensity-Adapted Algorithm With Adaptive Selection Strategy for the Fast Decision of H.264/AVC Intra-Prediction Modes

H.264/Advanced Video Coding (AVC) is well known for its superiority of finding an optimal tradeoff between the visual quality and the bit-rate expense. Nevertheless, the highly complex procedures of finding an optimal intra- or inter-prediction mode can degrade the run-time performance of the coding process. To speed up the run-time performance during the encoding of H.264/AVC intra-prediction mode, we apply in this paper a simple yet effective gradient evaluation approach so that the texture orientation inside the coding block can be evaluated efficiently. Moreover, we propose an adaptive selection strategy in this paper so that only a subset with a variable number of the intra-prediction modes will be sent for the rate-distortion optimization process. With the proposed gradient evaluation and adaptive selection strategy, a noticeable speedup on the run-time performance can be achieved with only a minor degradation on the visual quality and the bit-rate expense. When compared with the existing state-of-the-art fast decision algorithms, a significant improvement over prior arts on the proposed cost performance metric can be obtained, which demonstrates the superiority of the proposed approach.