Simplified Level Estimation for Rate-Distortion Optimized Quantization of HEVC

Xu, Motong; Canh, Thuong Nguyen

doi:10.1109/tbc.2019.2941063

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…Such kind of RD-based determination undoubtedly brings compression performance gains while in turn increasing the computational complexity. Experimental results in [29] on the latest HEVC test platform reported that RDOQ can achieve around 3% to 5% BD-Rate [30] savings along with 12% to 25% encoding time increment for HEVC. In the literature, there are two main strategies to achieve the low complexity RDOQ, including the statistics-based methods and the RD model based methods.…”

Section: Fast Rdoqmentioning

confidence: 99%

“…In [35] and [29], based on the observations that RDOQ tends to adjust the quantization level "1" to "0" for the coefficients locating at high frequency domain in larger TBs, an early quantization level decision scheme is proposed, which forces the quantization level to be zero without RDOQ process [29,35],…”

Section: A Statistics-based Fast Rdoqmentioning

confidence: 99%

“…The performance of several fast RDOQ schemes including Cui et al [42], He et al [41], Lee et al [33], Xu et al [29], Wang et al [40], Zhang et al [32] and Wang et al [31] are presented in Table 2, Table 3 and Table 4. The version of test platforms, as well as the performance of individual sequences are all presented.…”

Section: Rate-distortion Performance and Coding Complexitymentioning

confidence: 99%

“…The performance of RDOQ bypass method combined with ∆J estimation model proposed by Lee et al [33] is relatively conservative, as it introduces very negligible performance loss with moderate speedup. The statistical based fast RDOQ schemes proposed in [29] [40] [32] and [31] could bring 30% to 40% quantization time reductions.…”

Section: Rate-distortion Performance and Coding Complexitymentioning

confidence: 99%

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Low Complexity Quantization in High Efficiency Video Coding

et al. 2020

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The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency Video Coding (HEVC), leading to inspiring improvement in terms of rate-distortion performance. However, its heavy use imposes high complexity on the encoder in realworld video compression applications. In this paper, we provide a comprehensive review on low complexity quantization techniques in HEVC, including both fast RDOQ and all-zero block detection. In particular, the fast RDOQ relies on rate and distortion models for rate-distortion cost estimation, such that the most appropriate quantized coefficient is selected in a low complexity way. All-zero block detection infers the all-zero block by skipping transform and quantization, in an effort to further reduce the complexity. The relationship between the two techniques is also discussed, and moreover, we also envision the future design of low complexity quantization in the upcoming Versatile Video Coding (VVC) standard. INDEX TERMS High efficiency video coding, low complexity video coding, rate-distortion optimized quantization.

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Section: Fast Rdoqmentioning

confidence: 99%

Section: A Statistics-based Fast Rdoqmentioning

confidence: 99%

Section: Rate-distortion Performance and Coding Complexitymentioning

confidence: 99%

Section: Rate-distortion Performance and Coding Complexitymentioning

confidence: 99%

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Low Complexity Quantization in High Efficiency Video Coding

et al. 2020

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“…Virtual reference frame (VRF) is another inter coding improvement approach by (i) generating virtual frames and (ii) utilizing virtual frames as an additional reference or for guided reconstructed frames [8]. Several works utilized the motion estimation or advanced deep learning framework to interpolate frames from decoded frames [9][10][11][12][13]. The deep learning approach shows higher gain but also introduces tremendous complexity in both encoder and decoder [11][12][13].…”

Section: Introduction 1context and Motivationsmentioning

confidence: 99%

A Trellis Based Temporal Rate Allocation and Virtual Reference Frames for High Efficiency Video Coding

HoangVan¹,

Hue²,

Canh

2021

Electronics

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The High Efficiency Video Coding (HEVC) standard has now become the most popular video coding solution for video conferencing, broadcasting, and streaming. However, its compression performance is still a critical issue for adopting a large number of emerging video applications with higher spatial and temporal resolutions. To advance the current HEVC performance, we propose an efficient temporal rate allocation solution. The proposed method adaptively allocates the compression bitrate for each coded picture in a group of pictures by using a trellis-based dynamic programming approach. To achieve this task, we trained the trellis-based quantization parameter for each frame in a group of pictures considering the temporal layer position. We further improved coding efficiency by incorporating our proposed framework with other inter prediction methods such as a virtual reference frame. Experiments showed around 2% and 5% bitrate savings with our trellis-based rate allocation method with and without a virtual reference frame compared to the conventional HEVC standard, respectively.

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