视频编码标准HEVC中的环路滤波技术分析

新一代视频编码标准HEVC中增加了环路滤波技术来提高编码图像质量。研究了滤波过程中的两种算法:去块滤波和自适应采样点滤波。去块滤波可以消除块效应,自适应采样点滤波可以减小图像失真。通过实验仿真,分析了滤波技术的有效性。结果表明该滤波技术提高了编码性能,改善了图像质量。     

H.264中的去方块滤波

介绍了H.264视频编码标准中的自适应去方块滤波的原理、过程及参数选取,并在此基础上进行了仿真实验。实验结果表明,去方块滤波在提高图像质量和降低编码视频码率上效果显著。     

H.264去块滤波算法及硬件实现

H.264是ITU-T/ISO在2003年公布的最新的国际视频压缩编码标准,它大大提高了编码效率和图像质量,其中一个重要原因是在编解码环路中引入了去块滤波器。介绍了H.264视频编码标准中的去块滤波算法,并提出了一种可实现的去块滤波器硬件结构。该结构通过合理利用本地SRAM资源,大大减少了总线带宽需求,提高了硬件处理速度。仿真结果显示,通过该去块滤波器进行环路滤波,很大程度地消除了方块效应,图像质量得到明显改善。     

一种自适应的时空视频去噪算法

该文提出了一种基于运动估计的自适应时空视频去噪算法。算法直接嵌入在视频压缩编码中。空域上通过局部相似性寻找候选滤波点避免由噪声干扰形成的伪相似点,提高滤波点的有效性。时域上对各像素点进行运动预测,既增加了滤波点数量,减弱帧与帧之间的闪烁现象,又避免了因帧间运动而带来的运动模糊。另一方面,通过噪声强度和图像边缘信息自适应调节滤波窗口大小及滤波权重大小,实现了滤波强度自适应和空间细节的保护。     

基于H.264环路滤波算法的研究

H.264标准由于其编码的高效性和网络亲和性成了最新的国际视频编码标准;它引进了许多新的视频编码技术,编码效率比以往的标准提高了将近50%.标准中DCT变换之后的环路滤波技术有效地滤除了块效应使得图像质量有了很大程度的提高,并且在提高峰值信噪比和降低比特率方面起着重要作用.本文详细了介绍环路滤波过程及原理算法,并通过大量序列的仿真,验证了其不可缺少的作用.     

一种适用于AVS去块滤波器的硬件实现方法

设计一种自适应去块滤波器的硬件实现方法,采用垂直滤波和水平滤波交叉的处理顺序,通过使用寄存器组进行转置和存储中间数据,极大减小了访问带宽和片上存储资源,通过有效的控制机制实现了在289个时钟周期内对单一宏块数据进行去块滤波,可满足高清视频实时编码的需要.     

一种提升电视图像质量的自适应去隔行算法

提出了一种用于提升电视图像质量的自适应去隔行算法。该算法中,待重建像素先分别在时域与空域进行重建,然后综合处理得到最终的重建值。空域滤波部分采用基于边缘自适应的方法,能较理想地解决运动图像的边缘锯齿等效应;时域滤波部分则基于运动自适应的方法,有效地提高了运动检测的准确性。测试结果表明,该算法实现较简单,且能有效改善去隔行后的电视图像质量。     

H.264编码环中的去块效应滤波系统

介绍了H.264的编解码模型系统中的去块效应滤波系统，分析了该系统原理及其相对于以往去块效应滤波系统的改进。并通过仿真实验验证了该系统在提高图像质量和降低视频流码率上的较好作用。     

一种HEVC样点自适应补偿改进方法*

新一代视频编码标准HEVC在环路滤波技术中采用样点自适应补偿算法解决变换量化引起的振铃效应,包括边带补偿和边缘补偿,但是边带补偿算法没有完全考虑边带分布情况,仅传递4个连续的边带.利用图像直方图充分分析了边带分布情况,自适应地提取了帧内预测模式纹理信息,针对不同大小的树形编码块提出了一种样点自适应补偿改进方法.实验结果表明,与HM10.0相比,该方法提高了编码性能,尤其在色度上比特率增益可达到-7.53％,图像边缘部分主观评价质量提升效果明显.     

H.264去块滤波快速算法的设计与实现

介绍了H.264去块滤波的基本原理,并基于滤波强度预判的思想提出了一种快速去块滤波算法.通过软件实现验证了该算法在不影响解码图像质量的前提下较标准中的算法节省了约70%的滤波运算量,有效提高了软件解码器的运行速度,有助于H.264解码器实时应用的实现.     

Variable block-based deblocking filter for H.264/AVC on low-end and low-bit rates terminals

H.264/AVC supports variable block motion compensation, multiple reference frames, 1/4-pixel motion vector accuracy, and in-loop deblocking filter, compared with previous video coding standards. While these coding techniques are major functions for video compression improvement, they lead to high computational complexity at the same time. For the H.264 video coding techniques to be actually applied on low-end/low-bit rates terminals more extensively, it is essential to improve the coding efficiency. Currently the H.264 deblocking filter, which can improve the subjective quality of video, is hardly used on low-end terminals due to computational complexity.In this paper, we propose an enhanced method of deblocking filter that efficiently reduces the blocking artifacts occurring during the low-bit rates video coding. In the ‘variable block-based deblocking filter (VBDF)’ proposed in this paper, the temporal and spatial characteristics of moving pictures are extracted using the variable block-size information of motion compensation, the filter mode is classified into four different modes according to the moving-picture characteristics, and the adaptive filtering is executed in the separate modes. The proposed VBDF can reduce the blocking artifacts, prevent excessive blurring effects, and achieve about 30–40% computational speedup at about the same PSNR compared with the existing methods.     

Depth reconstruction filter for depth coding

Depth images represent the distances of scene elements from a camera in 3D space; their efficient coding is crucial for emerging applications such as free-viewpoint TV and 3D video. An in-loop reconstruction filter that improves the depth-coding performance as well as the rendering quality of virtual views based upon the coded depth is proposed.     

视频编码预处理算法研究

针对现有降噪采用时空域滤波、去块效应采用环路滤波/后处理这种分而治之方案的缺点,通过分析降噪的基本技术、低码率视频应用中块效应产生的原因和去块效应的常用方法,从滤除不需要或相对不重要的高频离散余弦变换(DCT,discrete cosine transform)系数角度提出了降噪和去块效应可同时处理的设想。新提出的预处理算法是根据图像纹理特性、运动情况和码率约束等,在图像的不同区域采用不同强度的自适应双边滤波,这样不仅去噪,滤除不重要的细节以便于高效压缩,避免块效应的产生,同时也可起到一定程度的码率控制效果。     

PTR-CNN for in-loop filtering in video coding

A deep learning method called PTR-CNN (Predicted frame with Transform unit partition and prediction Residual aided CNN) is proposed for in-loop filtering in video compression. To reduce the computational complexity of an end-to-end CNN in-loop filter, a non-learning method of reference frame selection is designed to select the highest quality frame based on the frame’s blurriness and smoothiness scores. The transform unit (TU) partition and the prediction residual (PR) of the current frame are used as extra inputs to the neural network as the filtering guidance. The selected similar and high quality reference frame (RF) and the current unfiltered frame (CUF) are input to a CNN based motion compensation module to generate a predicted frame (PF). Finally input the PF, the CUF, the CUF’s TU partition and the CUF’s PR into the main CNN to reconstruct the filtered frame. The model is implemented in Tensorflow and tested in HEVC and AV1. Experimental results show that the complexity of proposed PTR-CNN is less than SOTA CNN-based reference aided in-loop filtering methods and slightly outperforms their RD performance. The scheme introduces a complexity overhead of 7% on the encoder. In particular, for random access, the proposed model achieves 11.78% coding gain over HEVC with DBF/SAO off, while has a gain of 4.76% over HEVC with DBF/SAO on. Ablation study demonstrates that the RF contributes about 10% of the total gain, and the TU and PR contribute over 4% of the total one, proving the effectiveness of each module. Moreover, it is observed that the proposed method can restore detailed structures and textures and hence improve the subjective quality.     

A Novel SAO-based Filtering Technique for Reduction in Temporal Flickering Artifacts in H.265/HEVC

This paper presents a novel filtering technique based on sample adaptive offset (SAO) in H.265/high-efficiency video coding (HEVC) for reduction in the temporal flickering artifacts and improving the coding performance. SAO is a newly introduced technique for in-loop filtering in H.265/HEVC, which derives the offsets independently for each frame in the spatial domain without considering temporal frame correlation. As a result, the temporal distortion artifacts which will have a negative effect on the subjective quality, such as flickering artifacts, cannot be effectively addressed. In this paper, the rate-distortion optimization of the newly developed SAO method, referred to as Inter-SAO, is performed on the residual samples between adjacent frames. Inter-SAO and SAO in the reference software of H.265/HEVC (i.e., the test model HM) are then combined to form the novel in-loop filter-based method, denoted as 3D-SAO filtering method, where both spatial information and temporal information are effectively utilized to reduce the overall distortion in reconstructed videos. Compared with the SAO in HM, 3D-SAO has demonstrated its advanced performance for flickering artifacts suppression. Furthermore, 3D-SAO improves the coding efficiency compared with the SAO in HM with a performance gain of up to 0.91 dB in \(\Delta PSNR\), 1.74 dB in \(\Delta PSPNR\) and 7.33 % in BD-rate reduction.     

基于全相位IDCT滤波器的内插重采样分层编码技术

该文提出了一种基于全相位逆离散余弦变换(APIDCT)内插滤波器的重采样分层编码技术。全相位数字滤波器(APDF)是一种新型的线性相位滤波器。该文给出了APIDCT的设计方法,并通过比较几种APDF的性能,证明APIDCT在图像内插方面性能优异。在此内插方法基础上,结合重采样和自适应算术编码形成了分层图像编码技术,这种技术算法简单,适用于空间可分级编码。实验结果表明仅用其中3层,重建图像的峰值信噪比和压缩率就可达到略优于JPEG的效果,并且压缩率高时也不会出现块效应。     

Adaptive deblocking method for low bitrate coded HEVC video

HEVC in-loop deblocking filter significantly improves the subjective quality of coded video by removing blocking artifact. However, there are still visible blocking artifacts in the complex videos with fast and chaotic motions coded at a low bitrate. In this paper, we propose a three-step deblocking filter scheme, which pre-processes video to remove undesired noise, next removes the corner outliers, and then suppresses the normal blocking artifacts with adaptive deblocking filters. The whole deblocking filtering process is applied on both luma and chroma components. Experimental results show that the proposed method could effectively improve the subjective quality for various videos, and outperform other typical post-processing deblocking methods.     

A progressive CNN in-loop filtering approach for inter frame coding

Convolutional Neural Network (CNN) structures have been designed for in-loop filtering to improve video coding performance. These CNN models are usually trained through learning the correlations between the reconstructed and the original frames, which are then applied to every single reconstructed frame to improve the overall video quality. This direct model training and deployment strategy is effective for intra coding since a locally optimal model is sufficient. However, when applied to inter coding, it causes over-filtering because the intertwined reference dependencies across inter frames are not taken into consideration. To address this issue, existing methods usually resort to the Rate–Distortion Optimization (RDO) to selectively apply the CNN model, but fail to address the limitation of using a local CNN model. In this paper, we propose a progressive approach to train and incorporate the CNN-based in-loop filters to work seamlessly with video encoders. First, we develop a progressive training method to obtain the inter model. Using transfer learning, reconstructed frames using the CNN model are progressively involved back into the training of the CNN model itself, to simulate the reference dependencies in inter coding. Next, we design a frame-level model selection strategy for the high-bitrate coding where the over-filtering effect is diluted. Experimental results show that the proposed method outperforms the RDO method that utilizes only local model. Proposed approach also achieves comparable coding performance but with less computational complexity when integrating our progressive model into the RDO scheme.     

Adaptive rate-distortion optimal in-loop quantization for matching pursuit

In this paper, an adaptive in-loop quantization technique is proposed for quantizing inner product coefficients in matching pursuit. For each matching pursuit (MP) stage a different quantizer is used based on the probability distribution of MP coefficients. The quantizers are optimized for a given rate budget constraint. Additionally, our proposed adaptive quantization scheme finds the optimal quantizers for each stage based on the already encoded inner product coefficients. Experimental results show that our proposed adaptive quantization scheme outperforms existing quantization methods used in matching pursuit image coding.     

In-loop selective processing for contour artefact reduction in video coding

Proposed is an in-loop decontouring algorithm that utilises block-based processing for video coding. In order to avoid uniform filtering that brings about unnecessary processing, candidate blocks that may contain noticeable false contours using different kinds of features in a block are sequentially refined. Then, pseudorandom noise masking is applied to them to reduce contour artefacts. It is demonstrated that visual quality could be efficiently improved with a negligible rate-distortion (RD) loss.