Institute of Computer Science and Technology, Peking University, Beijing 100871, China.
IEEE Trans Image Process. 2013 Jan;22(1):202-14. doi: 10.1109/TIP.2012.2215618. Epub 2012 Aug 27.
This paper provides a systematic rate-distortion (R-D) analysis of the dead-zone plus uniform threshold scalar quantization (DZ+UTSQ) with nearly uniform reconstruction quantization (NURQ) for generalized Gaussian distribution (GGD), which consists of two aspects: R-D performance analysis and R-D modeling. In R-D performance analysis, we first derive the preliminary constraint of optimum entropy-constrained DZ+UTSQ/NURQ for GGD, under which the property of the GGD distortion-rate (D-R) function is elucidated. Then for the GGD source of actual transform coefficients, the refined constraint and precise conditions of optimum DZ+UTSQ/NURQ are rigorously deduced in the real coding bit rate range, and efficient DZ+UTSQ/NURQ design criteria are proposed to reasonably simplify the utilization of effective quantizers in practice. In R-D modeling, inspired by R-D performance analysis, the D-R function is first developed, followed by the novel rate-quantization (R-Q) and distortion-quantization (D-Q) models derived using analytical and heuristic methods. The D-R, R-Q, and D-Q models form the source model describing the relationship between the rate, distortion, and quantization steps. One application of the proposed source model is the effective two-pass VBR coding algorithm design on an encoder of H.264/AVC reference software, which achieves constant video quality and desirable rate control accuracy.
本文针对广义高斯分布(GGD)的零区加均匀门限标量量化(DZ+UTSQ)与几乎均匀重建量化(NURQ),进行了系统的率失真(R-D)分析,包括 R-D 性能分析和 R-D 建模两个方面。在 R-D 性能分析方面,首先推导出了最优熵约束下的 DZ+UTSQ/NURQ 的初步约束条件,在此约束条件下,阐明了 GGD 失真率(D-R)函数的特性。然后,对于实际变换系数的 GGD 源,在实际编码比特率范围内严格推导出最优 DZ+UTSQ/NURQ 的细化约束条件和精确条件,并提出了有效的 DZ+UTSQ/NURQ 设计准则,以便在实际中合理简化有效量化器的使用。在 R-D 建模方面,受 R-D 性能分析的启发,首先开发了 D-R 函数,然后使用分析和启发式方法推导出了新颖的率量化(R-Q)和失真量化(D-Q)模型。D-R、R-Q 和 D-Q 模型构成了描述率、失真和量化步长之间关系的源模型。该源模型的一个应用是在 H.264/AVC 参考软件的编码器上进行有效的两阶段 VBR 编码算法设计,该算法实现了恒定的视频质量和理想的率控制精度。