A Novel Hard-Decision Quantization Algorithm Based on Adaptive Deadzone Offset Model

In video encoder, the soft-decision quantization (SDQ) achieves superior coding performance however suffering from deadly sequential processing dependency. Comparatively, deadzone hard-decision quantization (HDQ) is dependency-free and suitable for hardwired parallel processing, however suffering from non-negligible coding performance degradation. In this paper, a content-adaptive HDQ algorithm is proposed by employing an adaptive deadzone offset. The contributions of this paper are characterized as follows. On one hand, this work applies offline statistic analysis, Bayes method, to explore the distribution characteristics of the desired deadzone offsets obtained from huge amounts of samples by fully simulating the behaviour of SDQ, and then derives adaptive deadzone offset model by maximizing the probability of right judgment of offset-induced rounding in HDQ. On the other hand, the deadzone offset model is constructed heuristically as functions of quantization step size, the distribution parameter of DCT coefficients and the number of possible significant coefficients prior to the current coefficient in the block. Simulation results verify that the proposed adaptive HDQ algorithm, in comparison with fixed-offset HDQ, achieves 0.08836 dB PSNR increment and 3.097 % bit rate saving with almost negligible complexity increase. In addition, this work, in comparison with the SDQ, achieves less than 0.03921 dB PSNR loss and 1.51 % bit rate increment. The proposed HDQ is well-suited for hardware encoder design.