A Motion Picture Coding Algorithm Using Adaptive DCT Encoding Based on Coefficient Power Distribution Classification

A motion picture coding algorithm using motion-compensated interframe prediction and the adaptive discrete cosine transform (DCT) encoding technique is proposed. High coding efficiency is obtained by the adaptive DCT encoding technique in which encoding parameters are fitted to widely varying characteristics of the interframe differential signal. Segmented DCT subblocks of interframe prediction error are classified into categories based on their coefficient power distribution characteristics. The adaptation gain results from using a suitable variable word length code set designated by the above classification for encoding each quantization index of DCT coefficients. In addition, a new coding parameter control method is introduced based on the information rate estimation of the current frame. This classification promotes high stability because good estimation accuracy of bits consumption for each DCT subblock is obtained by utilizing the category indexes. Simulation results show that the proposed algorithm has enough coding efficiency to transmit videoconferencing motion pictures through a 384 kbit/s channel.     

Entropy Coding for a Hybrid Scheme with Motion Compensation in Subprimary Rate Video Transmission

Entropy coding has been investigated for motion-compensated interframe (MC) prediction followed by two-dimensional discrete cosine transform (DCT) for prediction error. In particular, variable word length coding methods for motion vector and transform coefficients have been discussed assuming low bit rate such as 384 kbits/s for transmission of videoconference sequences. For motion vector information, it is advantageous to employ a one-dimensional code set common to both horizontal and vertical components of motion vectors. The code set can be obtained using a combined distribution of the two components. In order to encode transform coefficients, different methods are applied to significant and insignificant blocks. Run-length coding is adequate for representing clusters of insignificant blocks. In encoding transform coefficients in significant blocks, a zone coding method which encodes transform coefficients within a minimum area enclosing all nonzero coefficients is suitable. Simulation of video sequences shows that a combination of the coding methods described here can achieve high coding efficiency for videoconference sequences.     

Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior.

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.     

一种改进的可分级视频编码方法及其网络传输研究

该文提出了一种改进的可分级视频编码方法。为了适应流媒体的分层传输要求,该方法通过对DCT系数量化残差的位平面编码产生视频流的增强层部分,其基本层码流由更多的子基本层组成,各子基本层通过宏块级DCT系数重排及VLC重组生成。同时,该文设计了一种针对该分层视频流数据的网络传输自适应不等重丢包保护(AUPLP)策略,在估计当前可利用带宽资源的基础上,实时调整不同层数据的保护力度,并控制传输截断的层数。仿真结果表明,与传统方法相比该文方案在低带宽时可获得平均1.2dB的编码增益,AUPLP的应用也大大改善了视频流媒体的传输质量。     

Study on the distribution of DCT residues and its application to R-D analysis of video coding

Quantization errors in discrete-cosine-transform (DCT) video compression are known as DCT residues. Knowledge on their distribution is essential in understanding rate-distortion (R-D) behaviors of generic video coding. Traditional R-D analysis adopted a simplified distortion model. Those distortion models took only quantization parameter into account. They lack adaptability to variation of video sources, as the distribution of coding errors also depends on the statistics of video source. Another common approach models the distribution of DCT residues by fitting experimental data from coded pictures to conjectured statistical distributions, but it did not provide insights into what gives rise to the distribution of DCT residues. This paper intends to quantify the distribution of DCT residues with respect to video source and with respect to the quantization strategy by understanding the quantization of DCT frequency components. Moreover, it is applied to derive an R-D model to show the advantage of the proposed distribution model.     

Prioritized DCT for compression and progressive transmission ofimages

An approach is based on the block discrete cosine transform (DCT). The novelty of this approach is that the transform coefficients of all image blocks are coded and transmitted in absolute magnitude order. The resulting ordered-by-magnitude transmission is accomplished without sacrificing coding efficiency by using partition priority coding. Coding and transmission are adaptive to the characteristics of each individual image. and therefore, very efficient. Another advantage of this approach is its high progression effectiveness. Since the largest transform coefficients that capture the most important characteristics of images are coded and transmitted first, this method is well suited for progressive image transmission. Further compression of the image-data is achieved by multiple distribution entropy coding, a technique based on arithmetic coding. Experiments show that the approach compares favorably with previously reported DCT and subband image codecs.     

Object-Based Very Low Bit-Rate Coding Using Motion Parameter Estimation Based on Multiple Frame Prediction

This paper proposes an object-oriented coding method in very low bit-rate channels, using the motion estimation technique based on multiple frame prediction. In most existing methods, only the global motion components are transmitted. So the images containing abrupt motions and noise yield large error, even after texture compensation using the discrete cosine transform (DCT). To reduce the global motion prediction error due to usage of rigid motion parameters, residual image mapping is employed. Since high computational complexity is required for local motion correction using residual image mapping, simplified global motion prediction prior to local motion refinement parameter extraction is proposed to reduce the burden of the overall coder complexity. Computer simulation results show that the proposed method gives good performances in terms of the subjective quality, the peak signal to noise ratio (PSNR), and the compression ratio (CR).     

Online Dictionary Learning Based Intra-frame Video Coding

In this paper, we propose an online learning based intra-frame video coding approach, exploiting the texture sparsity of natural images. The proposed method is capable of learning the basic texture elements from previous frames with convergence guaranteed, leading to effective dictionaries for sparser representation of incoming frames. Benefiting from online learning, the proposed online dictionary learning based codec (ODL codec) is able to achieve a goal that the more video frames are being coded, the less non-zero coefficients are required to be transmitted. Then, these non-zero coefficients for image patches are further quantized and coded combined with dictionary synchronization. The experimental results demonstrate that the number of non-zero coefficients of each frame decreases rapidly while more frames are encoded. Compared to the off-line mode training, the proposed ODL codec, learning from video on the fly, is able to reduce the computational complexity with fast convergence. Finally, the rate distortion performance shows improvement in terms of PSNR compared with the K-SVD dictionary based compression and H.264/AVC for intra-frame video at low bit rates.     

Layered Wyner–Ziv Video Coding

Following recent theoretical works on successive Wyner-Ziv coding (WZC), we propose a practical layered Wyner-Ziv video coder using the DCT, nested scalar quantization, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information at the decoder). Our main novelty is to use the base layer of a standard scalable video coder (e.g., MPEG-4/H.26L FGS or H.263+) as the decoder side information and perform layered WZC for quality enhancement. Similar to FGS coding, there is no performance difference between layered and monolithic WZC when the enhancement bitstream is generated in our proposed coder. Using an H.26L coded version as the base layer, experiments indicate that WZC gives slightly worse performance than FGS coding when the channel (for both the base and enhancement layers) is noiseless. However, when the channel is noisy, extensive simulations of video transmission over wireless networks conforming to the CDMA2000 1X standard show that H.26L base layer coding plus Wyner-Ziv enhancement layer coding are more robust against channel errors than H.26L FGS coding. These results demonstrate that layered Wyner-Ziv video coding is a promising new technique for video streaming over wireless networks     

基于DCT域的任意分辨率视频可伸缩编码

空间分辨率可伸缩编码能够满足网络视频对不同码率的要求以及不同用户显示终端的需求,成为近几年研究热点.由此提出一种基于DCT域准卷积的任意分辨率下采样方法,该方法通过分析子块与宏块DCT系数之间的关系,实现了由子块DCT系数预测宏块DCT系数,通过低通滤波器截取预测的宏块低频DCT系数,实现任意分辨率下采样.实验证明:该方法适合诸如H.264、AVS等基于DCT变换的视频编码标准,且提供灵活的空间可伸缩能力.     

基于DCT变换的快速分形编码方法

本文提出了一种基于DCT变换加速相似块匹配的分形编码方法.通过图像块与相似块在DCT变换域少数低频系数的比较,来确定匹配的相似块.并根据DCT变换的性质,方便的确定对比度因子和亮度偏移量.并采用将图像块与相似块的DCT变换系数预制成查找表的方法减少重复运算.结果表明,编码时间大幅度减少,而PSNR只是略有下降.     

一种改进的MPEG-4精细可伸缩编码方法

提出了一种改进的MPEG-4精细可伸缩编码方法(巧GS)。在增强层编码中，IFGS增加一个高质量的参考来作为预测补偿，然后利用DCT残差系数的相关性来消除数据冗余。试验结果表明本文提出的改进方法比原来的FGS编码方法有更高的效率。     

Image postprocessing by Non-local Kuan’s filter

Blocking artifacts exist in images and video sequences compressed to low bit rates using block-based discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, two image postprocessing techniques, DNLK filter and OCDNLK filter, are presented in this paper. A more accurate DCT domain Kuan’s filter based on Non-local parameter estimation was proposed from the linear minimum mean-square-error (MMSE) criterion. We analyze the required two assumptions for the filter theoretically. Then the DCT domain Kuan’s filter for low frequency coefficients and Non-local mean filter for high frequency AC coefficients constitute the proposed Non-local Kuan’s (NLK) filter. After that, we propose the Dual Non-local Kuan’s (DNLK) filter by applying the proposed filter in dual layer. The DNLK filter is extended to form the Overcomplete Dual Non-local Kuan’s (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.     

Joint Scene and Signal Modeling for Wavelet-Based Video Coding with Cellular Neural Network Architecture

This paper presents a joint scene and signal modeling for the design of an adaptive quantization scheme applied to the wavelet coefficients in subband video coding applications. The joint modeling includes two integrated components: the scene modeling characterized by the neighborhood binding with Gibbs random field and the signal modeling characterized by the matching of the wavelet coefficient distribution. With this joint modeling, the quantization becomes adaptive to not only wavelet coefficient signal distribution but also the prominent image scene structures. The proposed quantization scheme based on the joint scene and signal modeling is accomplished through adaptive clustering with spatial neighborhood constraints. Such spatial constraint allows the quantization to shift its bit allocation, if necessary, to those perceptually more important coefficients so that the preservation of scene structure can be achieved. This joint modeling enables the quantization to reach beyond the limit of the traditional statistical signal modeling-based approaches which often lack scene adaptivity. Furthermore, the dynamically enforced spatial constraints of the Gibbs random field are able to overcome the shortcomings of the artificial block division which are usually the major source of distortion when the video is coded by block-based approaches at low bit rate. In addition, we introduce a cellular neural network architecture for the hardware implementation of this proposed adaptive quantization. We prove that this cellular neural network does converge to the desired steady state with the suggested update scheme. The adaptive quantization scheme based on the joint scene and signal modeling has been successfully applied to videoconferencing application and very favorable results have been obtained. We believe that this joint modeling-based video coding will have an impact on many other applications because it is able to simultaneously perform signal adaptive and scene adaptive quantization.     

波形内插语音编码器中快渐变波形幅度的量化

在低速率参数语音编码算法中,如何用有限的比特数有效地量化幅度谱是一个关键问题。本文对波形内插语音编码模型中快渐变波形幅度的量化问题进行了深入研究和分析,提出了一种基于矢量变维和DCT的REW幅度感觉加权量化方案,该方法降低了编码比特数,减少了存储和计算复杂度,增强了编码语音的感性质量。主观听力测试结果表明该量化方案在每帧4比特时的WI语音编解码质量要优于用基于DCT的REW幅度矩阵量化方案在每帧10比特时的重建语音质量。     

一种基于联合信源信道自适应的不等错误保护方法

针对误码信道和信源编码的统计特性,提出了一种能有效提高基于DCT变换的视频编码码流的抗误码性能的自适应不等错误保护方法.它基于信道误码的反馈信息,选择DCT的量化参数q和信道编码码率,并进行DCT重排和VLC编码,再对数据流进行不等差错保护.通过实验证明,它在高误码信道中性能优于EEP等.     

Reduced‐Resolution Intra Block Coding Mode

In this letter, a new intra‐block coding mode is presented to improve the coding efficiency for band‐limited signals. A band‐limited block is sub‐sampled, and the sub‐sampled signal is coded on the basis of the conventional prediction/transform coding. The rest of the samples are reconstructed by interpolation at the decoder side without any side information. Experimental results show that the proposed algorithm achieves coding gains of 2.7% for common intermediate format (CIF), 4.29% for quarter CIF, and 6.39% for 720p60 sequences against the H.264/AVC JM10.2 reference software.     

基于第二代Bandelet变换的图像编码

Bandelet基函数可以实现几何正则图像的最佳稀疏表示,在图像压缩方面有很大潜力。为了得到更高的压缩比,在第二代Bandelet变换的研究基础上,提出了一种与Bandelet变换相适应的图像编码方案。首先对Bandelet系数采用一致量化,重新组织系数的排布方式,然后设计了一种针对Bandelet系数的上下文模型,进行基于上下文的编码。对低频子带采用CALIC模型进行编码。实验表明,该编码方法对自然图像压缩是有效的,特别是对几何流特征比较明显的图像取得了优于JPEG 2000的结果。     

基于分类和陪集码的高光谱图像无损压缩

在基于陪集码的高光谱图像压缩算法中,由于按照编码块的最大残差确定整块无损压缩所需的码率存在较大冗余,该文提出了基于分类和陪集码的高光谱图像压缩算法。首先利用前一波段对应位置的预测噪声对当前波段编码块的像素进行分类,将具有相似相关性的像素归于一类,然后对每一类像素分别进行陪集码编码。实验表明分类可以有效地降低码率。和基于陪集码的算法相比,该文算法无损压缩的平均码率降低了大约0.4 bpp。     

参数重用的HEVC多描述视频编码

为了提高视频编码的容错性能,保证视频经不可靠信道传输后的重建质量。本文提出了一种面向高效视频编码标准（High Efficiency Video Coding,HEVC）的基于参数重用的多描述视频编码方法。原始视频进行空间梅花下采样,生成四个行列交错的子序列,其中两个子序列采用标准编码器进行编码,并在编码过程中提取视频中每个编码单元（Coding Unit,CU）的深度信息、预测单元（Predicting Unit,PU）的分割方式以及帧内预测模式。而其余两个子序列利用已编码的视频序列信息,进行简化的编码过程。选取一个经标准编码的子序列,与一个简化编码的子序列,结合生成描述1,其余子序列生成描述2,不同描述分信道传输。多描述的编码结构可以保证即使只接收到单一描述也能保证视频的重建质量,参数重用的方法利用子序列间的相关性,减少了冗余信息,降低了编码开销。实验结果表明,参数重用的HEVC多描述视频编码针对高清视频编码效果明显,边缘解码质量PSNR值仅略低于中心解码0.7 dB,有效地提高了高清视频编码的容错性能。进行简化编码子序列的平均编码时间节省了91.7%,实现了高编码效率、低复杂度的HEVC容错编码。