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

Wang, Hongkui; Yin, Haibing; Shen, Ye

doi:10.1007/978-3-319-48896-7_33

Cited by 2 publications

(9 citation statements)

References 7 publications

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“…However, there are still room for performance improvement. This work is an expansion of the previous work in [33].…”

Section: Introductionmentioning

confidence: 86%

“…The disadvantage of HDQ, however, lies in its inferior RD performance. Therefore, it would be desirable to develop an adaptive deadzone offset model to improve the traditional fixed-offset HDQ by learning the inner characteristics of SDQ [33], [37].…”

Section: Challenges and Motivationmentioning

confidence: 99%

“…RDOQ [13], and the conventional deadzone HDQ [34] are all taken as performance comparison anchors. Due to this paper is an expansion of our previous work in [33], the corresponding deadzone HDQ proposed in [33] is also as performance comparison anchor. These quantization algorithms are applied in both the final mode coding and the rate distortion optimized mode decision loop.…”

Section: A Performance and Accuracymentioning

confidence: 99%

“…The authors had made superficial researches on algorithm design for fast SDQ [14] and efficient HDQ [33]. In [14], a fast SDQ algorithm was proposed to decrease the number of trellis search stages to decrease the complexity and to break data dependency in optimal SDQ.…”

Section: Introductionmentioning

confidence: 99%

“…The inter-coefficient dependency was considerably decreased in [14], however there are still existing dependency that is harmful for latencysensitive video coders. In [33], heuristic method statistical analysis was applied to derive relatively accurate deadzone offset models for improving HDQ algorithm. However, there are still room for performance improvement.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Hard-Decision Quantization Using an Adaptive Deadzone Offset Model for Video Coding

et al. 2019

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In video coding, rate distortion optimized quantization (RDOQ), a popular version of softdecision quantization (SDQ), achieves superior coding performance, however is ill-suited for hardware implementation due to its inherent sequential processing. On the other hand, deadzone hard-decision quantization (HDQ) is friendly to hardware implementation, however suffers from non-negligible coding performance degradation. This paper proposes a content-adaptive deadzone offset model to improve the coefficient-wise deadzone HDQ by imitating the behavior patterns of RDOQ. The contributions of this paper are characterized by twofold. On one hand, this work formulates seeking optimal deadzone offset model as a problem of binary classification, and analyzes the distribution characteristics of the optimal deadzone offsets obtained from samples by fully imitating RDOQ, and then derives adaptive deadzone offset model by maximizing the right classification probability of offset-induced rounding in HDQ. On the other hand, the distribution parameters of DCT coefficients are measured in a position-wise way, and the adaptive deadzone model is built by applying Maximum a posterior estimation method according to three characteristic parameters, i.e. quantization step size, parameter of DCT coefficients, and quantization remainder, in the sense of rate distortion optimization. Simulation results verify that the proposed adaptive HDQ algorithm, in comparison with fixed-offset HDQ, achieves 0.54% and 0.52% bit rate saving on average with almost negligible complexity increment. Simultaneously, the proposed algorithm only sacrifices smaller than 0.55% and 0.54% increment in terms of BD-BR in comparison with RDOQ. The proposed HDQ is well-suited for hardwired video coding implementation.

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“…However, there are still room for performance improvement. This work is an expansion of the previous work in [33].…”

Section: Introductionmentioning

confidence: 86%

Section: Challenges and Motivationmentioning

confidence: 99%