Perceptually Adaptive Lagrangian Multiplier for HEVC Guided Rate-Distortion Optimization

Rouis, Kais; Larabi, Mohamed–Chaker; Tahar, Jamel Belhadj

doi:10.1109/access.2018.2843384

Cited by 15 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, both features considered in this algorithm are provided only for an adaptive λ in RDO. As a conclusion, spatial and temporal perceptual features for an adaptive QP decision can provide a better trade-off [43], [44].…”

Section: B Existing Methods Of Perceptual Adaptive Qp Selection For Hmmentioning

confidence: 80%

“…Xiang et al [43] proposed a perceptual motion estimation method using a spatial-temporal just-noticeable-distortion (JND) model for a QP offset design. Rouis et al [44] generated perceptual features temporally as well as CTU visual sensitivity for spatial features. However, both features considered in this algorithm are provided only for an adaptive λ in RDO.…”

Section: B Existing Methods Of Perceptual Adaptive Qp Selection For Hmmentioning

confidence: 99%

“…It can adjust the QP value for a distinctive frame or slice according to different spatial, temporal, or visual aspects. Some studies have discovered approaches to improve the compression rates [33]- [37] or visual quality [38]- [44] with various adaptive QP techniques. Typically, these studies prioritize the determination of optimum QPs for the RDO process to produce better encoding parameters by analyzing the QP-λ relationship or by observing the effectiveness of spatial-temporal dependencies among the basic units.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Perceptual Adaptive Quantization Parameter Selection Using Deep Convolutional Features for HEVC Encoder

Marzuki

Sim

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we propose a perceptual adaptive quantization based on a deep neural network on high efficiency video coding (HEVC) for bitrate reduction while maintaining subjective visual quality. The proposed algorithm adaptively determines frame-level QP values for different picture types of the hierarchical coding structure in HEVC by taking into account the high-level features extracted from the original and previously reconstructed pictures. A predefined model based on the visual geometry group (VGG-16) network is exploited to extract the high-level features for subjective visual characteristics. Furthermore, the Lagrange multiplier for each frame is also adaptively determined by involving the proposed features for deciding the appropriate parameter of the Lagrange multiplier that can be used for rate-distortion optimization during the encoding process. Experimental results reveal that the proposed perceptual adaptive QP selection can facilitate bitrate savings up to 65.73% and 47.68% and improve the BD-rate based on SSIM by approximately 20.68% and 14.27% under low-delay-P and random-access coding structures, respectively, with very minimal visual quality degradation when compared to HM-16.20 without adaptive QP selection. INDEX TERMS Adaptive quantization parameter, deep neural network, high efficiency video coding (HEVC), perceptual quantization parameter, VGG-16 network, video coding.

show abstract

Section: B Existing Methods Of Perceptual Adaptive Qp Selection For Hmmentioning

confidence: 80%

Section: B Existing Methods Of Perceptual Adaptive Qp Selection For Hmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual Adaptive Quantization Parameter Selection Using Deep Convolutional Features for HEVC Encoder

Marzuki

Sim

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Both gains have been analyzed quantitatively. So, the former has been measured in terms of BD-DMOS by computing the area between the lines (represented in purple in Figure 12) by means of a vertical integration [42], [43]. Results point Regression on the bit rate-DMOS values including the area between the lines where the BD-DMOS is computed by vertical integration.…”

Section: Test Resultsmentioning

confidence: 99%

Retinal Noise Emulation: A Novel Artistic Tool for Cinema That Also Improves Compression Efficiency

et al. 2020

View full text Add to dashboard Cite

In cinema it is standard practice to improve the appearance of images by adding noise that simulates film grain. This is computationally very costly, so it is only done in post-production and not on the set. It is also limiting because the artists are not able to really experiment with the noise nor introduce novel looks. Furthermore, video compression requires a higher bit rate when the source material has film grain or any other type of high frequency texture. In this work, we introduce a method for adding texture to digital cinema that aims to solve these problems. The proposed algorithm is based on modeling retinal noise, with which the images processed by our method have a natural appearance. This ''retinal grain'' serves a double purpose. One is aesthetic, as it has parameters that allow to vary widely the resulting texture appearance, which make it an artistic tool for cinematographers. Results are validated through psychophysical experiments in which observers, including cinema professionals, prefer our method over film grain synthesis methods from academia and the industry. The other purpose of the retinal noise emulation method is to improve the quality of compressed video by masking compression artifacts, which allows to lower the encoding bit rate while preserving image quality, and to improve image quality while keeping the bit rate fixed. The effectiveness of our approach for improving coding efficiency, with average bit rate savings of 22.5%, has been validated through psychophysical experiments using professional cinema content shot in 4K, color-graded and where the amount of retinal noise was selected by a motion picture specialist based solely on aesthetic preference.

show abstract

“…Ferroukhi et al propose a method combining bandelets and the set partitioning in hierarchical trees (SPIHT) algorithm for medical video coding [ 12 ], and this method has shown good high performances in terms of visual quality and peak signal-to-noise rate (PSNR) at low bitrates. Rouis et al present a novel approach for perceptually guiding the rate-distortion optimization (RDO) process in H.265/HEVC [ 13 ], and this proposed method demonstrates a superior rate-distortion (R-D) performance over a compared approach adopting a similar scheme. Moreover, the learning-based perceptual video coding methods are proposed to improve video quality [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

Jiang

Song

Zhu

et al. 2019

Entropy

View full text Add to dashboard Cite

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application.

show abstract

Perceptually Adaptive Lagrangian Multiplier for HEVC Guided Rate-Distortion Optimization

Cited by 15 publications

References 35 publications

Perceptual Adaptive Quantization Parameter Selection Using Deep Convolutional Features for HEVC Encoder

Perceptual Adaptive Quantization Parameter Selection Using Deep Convolutional Features for HEVC Encoder

Retinal Noise Emulation: A Novel Artistic Tool for Cinema That Also Improves Compression Efficiency

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

Contact Info

Product

Resources

About