Adaptive Loop Filtering for Video Coding

Tsai, Chia-Yang; Chen, Ching-Yeh; Yamakage, Tomoo; Chong, In Suk; Huang, Yu-Wen; Fu, Chih-Ming; Itoh, Takayuki; Watanabe, Takashi; Chujoh, Takeshi; Karczewicz, Marta; Lei, Shawmin

doi:10.1109/jstsp.2013.2271974

Cited by 131 publications

(60 citation statements)

References 5 publications

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“…(5) with different reliabilities. In this paper, we take variance of the local prediction errors to reflect prediction reliability of TAR.…”

Section: Temporal Autoregressive Estimationmentioning

confidence: 99%

“…These methods can be classified into two categories, i.e., in-loop methods and post-processing methods. The in-loop filtering methods process a compressed frame within the coding loop, and the resulting frame can be utilized as reference to provide prediction for the coding of future frames, Examples of this category include the deblocking filter in H.264/AVC [2], Sample Adaptive Offset (SAO) [3] in HEVC, and the widely discussed adaptive loop filter (ALF) [4] [5] in HEVC development. These methods mainly take advantage of the local smoothness in image and adjust the filtering strength according to coding information, such as coding modes, quantization parameters (QP) and rate-distortion cost [6], to achieve a good reconstruction quality.…”

Section: Introductionmentioning

confidence: 99%

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Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction

Zhang

Xiong

Wang

et al. 2015

IEEE Trans. on Image Process.

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Annoying compression artifacts exist in most of lossy coded videos at low bit rates, which are caused by coarse quantization of transform coefficients or motion compensation from distorted frames. In this paper, we propose a compression artifact reduction approach that utilizes both the spatial and the temporal correlation to form multi-hypothesis predictions from spatio-temporal similar blocks. For each transform block, three predictions with their reliabilities are estimated, respectively. The first prediction is constructed by inversely quantizing transform coefficients directly, and its reliability is determined by the variance of quantization noise. The second prediction is derived by representing each transform block with a temporal auto-regressive (TAR) model along its motion trajectory, and its corresponding reliability is estimated from local prediction errors of the TAR model. The last prediction infers the original coefficients from similar blocks in non-local regions, and its reliability is estimated based on the distribution of coefficients in these similar blocks. Finally, all the predictions are adaptively fused according to their reliabilities to restore high-quality videos. The experimental results show that the proposed method can efficiently reduce most of the compression artifacts and improve both subjective and objective quality of block transform coded videos.

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“…(5) with different reliabilities. In this paper, we take variance of the local prediction errors to reflect prediction reliability of TAR.…”

Section: Temporal Autoregressive Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction

Zhang

Xiong

Wang

et al. 2015

IEEE Trans. on Image Process.

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show abstract

“…The number of taps in the filter is greater than nine due to symmetry; however, every picture may not need all the nine taps as in HM-7.0 [6]. This test model has different filter square shapes 5X5, 7X7 and 9X9, where these shapes can be selected for different pictures.…”

Section: Hevc Adaptive Loop Filteringmentioning

confidence: 99%

Adaptive Trilateral Filter for In-Loop Filtering

Kesireddy¹,

El-Sharkawy

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…In total, three built-in in-loop filters were proposed for standard HEVC, including deblocking filter (DBF) [2], sample adaptive offset (SAO) filter [3] and adaptive loop filter (ALF) [4]. Specifically, DBF is firstly used to remove the blocking artifacts.…”

Section: Introductionmentioning

confidence: 99%

A DenseNet Based Approach for Multi-frame In-loop Filter in HEVC

Yang

et al. 2019

2019 Data Compression Conference (DCC)

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High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches.

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Adaptive Loop Filtering for Video Coding

Cited by 131 publications

References 5 publications

Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction

Video Compression Artifact Reduction via Spatio-Temporal Multi-Hypothesis Prediction

Adaptive Trilateral Filter for In-Loop Filtering

A DenseNet Based Approach for Multi-frame In-loop Filter in HEVC

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