Macroblock Classification Research Articles

Motion Detection in Compressed Video Using Macroblock Classification

In this paper, to detect the moving objects between frames in compressed video and to obtain the best compression video and the noiseless video. We describe a video in which frames by classifying macroblocks (MB), and describe motion estimation (ME), motion vector field (MV) and motion compensation (MC). we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content based on the motion vector. MB class information video applications such as shot change detection, motion discontinuity detection, Outlier rejection for global motion estimation. To reduce the noise and to improve the clarity of the compressed video by using contrast limited adaptive histogram equalization (CLAHE) Algorithm.

Macroblock Classification Method for Video Applications Involving Motions

In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame. Based on the proposed macroblock classification, we further propose algorithms for different video processing applications, including shot change detection, motion discontinuity detection, and outlier rejection for global motion estimation. Experimental results demonstrate that the methods based on the proposed approach can work effectively on these applications.

Consistent-degradation macroblock grouping for parallel video streams over DiffServ networks

This paper presents a consistent-degradation macroblock grouping scheme for improving loss resilience of parallel video streams over a two-class DiffServ network. By jointly exploiting the H.264 flexible macroblock ordering (FMO) tool, a multi-stream macroblock ordering framework is designed to classify all macroblocks of a super-frame into two categories: important macroblocks as high-reliability traffic class and unimportant macroblocks as best-effort traffic class. To ensure efficiency and fairness of multi-stream macroblock ordering, the compound transmission distortion is introduced to measure both worst-case and average-case loss resilience of parallel video streams. The proposed consistent-degradation macroblock grouping scheme aims to further reduce the compound transmission distortion based on a GOP-level macroblock importance metric, which is adopted to timely estimate the error-propagation effect of macroblock loss on video quality degradation. With finer macroblock classification, the proposed scheme can find out an optimal grouping policy for each super-frame under the given transmission resource constraints. Both the theoretical analysis and simulation results show that the proposed scheme can effectively reduce the compound transmission distortion of parallel video streams over a two-class DiffServ network, and thus provide more consistent loss resilience and better utilization of transmission resources.

Real-time H.264 video encoding in software with fast mode decision and dynamic complexity control

This article presents a novel real-time algorithm for reducing and dynamically controlling the computational complexity of an H.264 video encoder implemented in software. A fast mode decision algorithm, based on a Pareto-optimal macroblock classification scheme, is combined with a dynamic complexity control algorithm that adjusts the MB class decisions such that a constant frame rate is achieved. The average coding efficiency of the proposed algorithm was found to be similar to that of conventional encoding operating at half the frame rate. The proposed algorithm was found to provide lower average bitrate and distortion than static complexity scaling.

Macroblock classification for complexity management of video encoders

Typically, many macroblocks (MBs) are skipped during encoding of H.263 or MPEG-4 SP video data, particularly at low bit-rates. In this paper, we describe an algorithm that predicts the occurrence of skipped MBs prior to encoding, making it possible to save significant computational effort by not coding these MBs. The algorithm estimates the energy of low-frequency quantized coefficients in order to classify each MB as ‘skipped’ or ‘not skipped’. Results show that the algorithm can deliver substantial computational savings at the expense of a small reduction in rate-distortion performance.

Optimum bit allocation and accurate rate control for video coding via ρ-domain source modeling

We present a new framework for rate-distortion (R-D) analysis, where the coding rate R and distortion D are considered as functions of /spl rho/ which is the percentage of zeros among the quantized transform coefficients. Previously (see He, Z. et al., Int. Conf. Acoustics, Speech and Sig. Proc., 2001), we observed that, in transform coding of images and videos, the rate function R(/spl rho/) is approximately linear. Based on this linear rate model, a simple and unified rate control algorithm was proposed for all standard video coding systems, such as MPEG-2, H.263, and MPEG-4. We further develop a distortion model and an optimum bit allocation scheme in the /spl rho/ domain. This bit allocation scheme is applied to MPEG-4 video coding to allocate the available bits among different video objects. The bits target of each object is then achieved by our /spl rho/-domain rate control algorithm. When coupled with a macroblock classification scheme, the above bit allocation and rate control scheme can also be applied to other video coding systems, such as H.263, at the macroblock level. Our extensive experimental results show that the proposed algorithm controls the encoder bit rate very accurately and improves the video quality significantly (by up to 1.5 dB).

Image sequence macroblock classification using neural networks

In this paper, a kind of modification to the standard back propagation (SBP) algorithm has been proposed for a real-world macroblock classification problem in video coding. The proposed algorithm studies the effect of adding variance-adaptive noise to the inputs, outputs, and weight connections of multilayer feedforward neural networks during SBP training. Simulations show that the variance-adaptive weight noise is found to be more effective in improving the generalization performance of our problem. Meanwhile adaptive quantization results for moving picture experts group 2 (MPEG-2) video coding confirm the efficacy of the proposed classification method such that it can consistently produce good subjective quality and objective quality sequences at any fixed bit rate. Furthermore, due to the neural networks' fault-tolerant nature and inherent parallelism, potential exits for VLSI implementation as a small cell.