Macroblock Coding Research Articles

H.264/AVC Video Encryption Based on Adaptive Permutation of Macroblock Coding Information

H.264/AVC Video Encryption Based on Adaptive Permutation of Macroblock Coding Information

Region-of-interest based rate control algorithm for H.264/AVC video coding

Conventional rate control algorithms allocate bits for every macroblock (MB) without consider whether it needs encoding, and they choose encoding mode only from the set provided by H.264/AVC standard. While, according to the human visual system (HVS) research, human eyes can only focus on one area in a frame, which is called region-of-interest (ROI). This phenomenon gives a chance to code all MBs unequally, especially for NROI MB, we may do not need encoded. In this paper, a ROI-based rate control algorithm for H.264/AVC video coding is proposed to improve the coding efficiency. After the ROI segmentation, the target bits are unequally allocated for each MB. For non-region-of-interest (NROI) MB coding, we proposed the Active MB Concealment (AMC) Mode in rate-distortion optimization (RDO). The AMC gives a tradeoff between the rate and distortion and improve the quality of the ROI MBs at the cost of the quality decreasing of the NROI MBs. Compared with the conventional rate control algorithms, the experimental results demonstrate that the proposed algorithm gives the better performance in terms of the average peak signal-to-noise ratio (PSNR) and the subjective evaluations, and maintains the smoothness of video quality.

A Noise Robust Mode Decision Algorithm Based on Macroblock-level Motion Detection for H.264/AVC

When video noise increases, the coding efficiency of the H.264/AVC considerably decreases. Therefore, we propose a novel mode decision algorithm based on early macroblock-level motion detection. According to the macroblock motion levels, four different mode decision strategies are chosen for current macroblock coding. Experimental results show that the proposed algorithm can significantly reduce the encoding time with similar coding efficiency when weighed up against the H.264/AVC.

Low complexity intra MB encoding in AVC/H.264

In this paper we introduce and evaluate a novel machine learning based approach to reduce the complexity of Intra macroblock (MB) coding. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264/AVC video have a correlation with the intensities of adjacent MBs and sub-MBs. This paper also discusses and analyzes different approaches of using machine learning in Intra prediction. We discuss, amongst other features, slices, Intra prediction scheme for H.264 and data mining. We use data mining algorithms to develop decision trees for H.264 coding mode decisions. The proposed approach reduces the H.264/AVC MB mode computation process into a decision tree lookup with very low complexity. The proposed algorithm is implemented in reference software by modifying the source code and is compared with the JM reference software for H.264/AVC.

Robust Streaming of Offline Coded H.264/AVC Video Via Alternative Macroblock Coding

An error resilient video streaming scheme that transmits offline coded H.264/AVC video through erroneous channels, called the alternative macroblock coding (AMC) scheme, is proposed in this work. In the AMC scheme, each macroblock can be reconstructed from one default and several alternative versions coded using different predictions. During the transmission, the sender tracks ACK or NACK messages from the receiver to detect transmission errors. When the reference used in the default version of a macroblock that has not been transmitted is corrupted by errors, one of its alternative versions that has the correctly received reference is selected to replace the default version in the output bit stream to stop error propagation. The AMC scheme is designed to ensure that the bit stream replacement during streaming will not cause significant mismatch at the decoder end and neither introduce large bit rate overhead to the transmitted bit stream. Furthermore, adaptive quantization selection and bit stream replacement methods are developed to improve the rate-distortion performance of received video. It is demonstrated by experimental results that the AMC scheme is effective in reducing error propagation in offline coded H.264/AVC video.

Low-Complexity Heterogeneous Video Transcoding Using Data Mining

Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications.

A Fast MB Mode Decision Algorithm for MPEG-2 to H.264 P-Frame Transcoding

The H.264 standard achieves much higher coding efficiency than the MPEG-2 standard, due to its improved inter-and intra-prediction modes at the expense of higher computational complexity. Transcoding MPEG-2 video to H.264 is important to enable gradual migration to H.264. However, given the significant differences between the MPEG-2 and the H.264 coding algorithms, transcoding is a much more complex task and new approaches to transcoding are necessary. The main problems that need to be addressed in the design of an efficient heterogeneous MPEG-2/H.264 transcoder are: the inter-frame prediction, the transform coding and the intra-frame prediction. In this paper, we focus our attention on the inter-frame prediction, the most computationally intensive task involved in the transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for P-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast MB mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and construct decision trees to classify the incoming MPEG-2 MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the MB mode selection complexity by as much as 95% while maintaining the coding efficiency. Finally, we conduct a comparative study with some of the most prominent fast inter-prediction methods for H.264 presented in the literature. Our results show that the proposed approach achieves the best results for video transcoding applications.

Digital Encoding Applied to Sign Language Video

We report a specially designed encoding technique for sign language video sequences supposing that the technique is for sign telecommunication such as that using mobile videophones with a low bitrate. The technique is composed of three methods: gradient coding, precedence macroblock coding, and not-coded coding. These methods are based on the idea to distribute a certain number of bits for each macroblock according to the evaluation of importance of parts of the picture. They were implemented on a computer and encoded data of a short clip of sign language dialogue was evaluated by deaf subjects. As a result, the efficiency of the technique was confirmed.

Macroblock‐based progressive fine granularity scalable video coding

AbstractA novel highly efficient macroblock‐based progressive fine granularity scalable (MBPFGS) video coding scheme that can optimally balance drifting errors and coding efficiency is proposed in this article. In order to improve coding efficiency, the existing PFGS video coding scheme uses a set of high‐quality references to predict the enhancement layers. However, potential drifting errors may arise because high‐quality references may not always be available at the decoder when channel bandwidth fluctuates. To solve this problem, we first analyze when drifting errors occur and how they will propagate. Then, an iterative model is established to estimate the drifting errors at the encoder. Meanwhile, three new INTER modes are proposed for the coding of macroblocks at the enhancement layers. One of these modes provides an effective and flexible method to reduce the drifting errors at low enhancement bit rates. Furthermore, we present a decision‐making mechanism to select the proper coding mode for each macroblock at the enhancement layers. Experimental results show that the proposed techniques can dramatically reduce drifting errors at low enhancement bit rates, while significantly improving coding efficiency, up to 2.3 dB in average PSNR, compared with MPEG‐4 FGS at moderate or high bit rates. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 297–307, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10071

Evaluating MPEG-4 video decoding complexity for an alternative video complexity verifier model

MPEG-4 is the first object-based audiovisual coding standard. To control the minimum decoding complexity resources required at the decoder, the MPEG-4 Visual standard defines the so-called video buffering verifier mechanism, which includes three virtual buffer models, among them the video complexity verifier (VCV). This paper proposes an alternative VCV model, based on a set of macroblock (MB) relative decoding complexity weights assigned to the various MB coding types used in MPEG-4 video coding. The new VCV model allows a more efficient use of the available decoding resources by preventing the overevaluation of the decoding complexity of certain MB types and thus making it possible to encode scenes (for the same profile@level decoding resources) which otherwise would be considered too demanding.

An HDTV-to-SDTV spatial transcoder

Both high-definition television (HDTV) and, standard-definition television (SDTV) use the MPEG-2 video coding standard, but they have different spatial resolutions. In order to support the interlaced video coding, MPEG-2 incorporates various macroblock prediction modes. Thus, the HDTV-to-SDTV transcoding needs to handle spatial resolution downscaling and various MEPG-2 macroblock prediction modes. We investigate schemes to exploit the correlations between the input and output video in the design of an HDTV-to-SDTV transcoder so that the computation can be greatly saved while the quality of video is preserved as much as possible. First, by utilizing the motion vectors and macroblock coding modes of the input video, efficient motion reestimation and macroblock mode decision algorithms are proposed. Then a novel picture target bit allocation algorithm taking advantage of the coding statistics of the input video is presented. Simulation results showing the effectiveness of the proposed approaches are also presented.

Object boundary padding technique for improvingMPEG-4 video compression efficiency

In MPEG-4, the boundary macro-blocks of reference video objects are padded by replicating the boundary samples towards the exterior. The authors propose the introduction of a linear extrapolation based pre-padding step to minimise prediction errors. Experimental results indicate that gains of up to 7% can be achieved for boundary macro-block coding by this proposed technique.