A reconfigurable H.264 video encoder hardware

In the new video coding standard H.264/AVC, motion estimation (ME) is allowed to search multiple reference frames. Therefore, the required computation is highly increased, and it is in proportion to the number of searched reference frames. However, the reduction in prediction residues is mostly dependent on the nature of sequences, not on the number of searched frames. Sometimes the prediction residues can be greatly reduced, but frequently a lot of computation is wasted without achieving any better coding performance. In this paper, we propose a context-based adaptive method to speed up the multiple reference frames ME. Statistical analysis is first applied to the available information for each macroblock (MB) after intra-prediction and inter-prediction from the previous frame. Context-based adaptive criteria are then derived to determine whether it is necessary to search more reference frames. The reference frame selection criteria are related to selected MB modes, inter-prediction residues, intra-prediction residues, motion vectors of subpartitioned blocks, and quantization parameters. Many available standard video sequences are tested as examples. The simulation results show that the proposed algorithm can maintain competitively the same video quality as exhaustive search of multiple reference frames. Meanwhile, 76 %-96 % of computation for searching unnecessary reference frames can be avoided. Moreover, our fast reference frame selection is orthogonal to conventional fast block matching algorithms, and they can be easily combined to achieve further efficient implementations.

In this paper, we propose a low power adaptive baseline H.264 video encoder hardware for portable devices. Multiple reference frame motion estimation (MRF ME) used in H.264 standard increases the video coding efficiency at the expense of increased computational complexity and power consumption. In the proposed hardware, the number of reference frames used for MRF ME can be dynamically changed for each macroblock in order to trade-off video coding efficiency and power consumption. The proposed hardware can code 55 CIF (352×288) frames per second with low hardware cost. Its power consumption ranges between 115mW and 235mW depending on the number of reference frames used for MRF ME.

A data reuse algorithm for multiple reference frame motion estimation is described. The proposed algorithm reduces memory access by modifying the reference frame search order and search centre such that the likelihood of data reuse is increased. Experimental results show that the algorithm reduces memory access by 15–30% compared to the conventional fast reference frame selection algorithm, while maintaining similar bit rate and PSNR.

Weighted prediction is a video coding tool to encode scenes with brightness variations. However, no single WP model works well for all types of brightness variations. In this paper, a novel single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in different macroblocks of the current frame even when they are predicted from the same reference. It provides this feature by making a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. Experimental results show that the proposed SRefMWP can improve prediction in scenes with different types of brightness variations, and even benefit to scenes that contain local brightness variation.

This paper presents a novel region-based weighted prediction (WP) parameter estimation scheme for H.264 to encode scenes with local brightness variations (LBV). The scheme is achieved by utilizing multiple WP parameter sets for various regions and assigning them to the same reference frame. These multiple WP parameter sets are then encoded using the framework of multiple reference frame motion estimation (MRFME). This arrangement ensures that the proposed scheme is compliant with the H.264 standard. In this scheme, a region partitioning process is designed to divide the current frame into different regions where each one has some degree of uniformity in its brightness variation. This can assist in estimating multiple sets of region-based WP parameters accurately. As a consequence, the proposed scheme can improve prediction in scenes with different degrees of LBVs in different regions of the same picture. Results show that the region-based scheme can efficiently handle LBV scenes with significant coding gain. (4 pages)

Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this paper, a new technique for efficient motion estimation is proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with a state of the art motion estimation algorithm and demonstrate significantly reduced motion estimation time while maintaining PSNR performance.

In H.264/AVC standard, motion estimation can be processed on multiple reference frames (MRF) to improve the video coding performance. For the VLSI real-time encoder, the heavy computation of fractional motion estimation (FME) makes the integer motion estimation (IME) and FME must be scheduled in two macro block (MB) pipeline stages, which makes many fast MRF algorithms inefficient for the computation reduction. In this paper, two algorithms are provided to reduce the computation of FME and IME. First, through analyzing the block's Hadamard transform coefficients, all-zero case after quantization can be accurately detected. The FME processing in the remaining frames for the block, detected as all-zero one, can be eliminated. Second, because the fast motion object blurs its edges in image, the effect of MRF to aliasing is weakened. The first reference frame is enough for fast motion MBs and MRF is just processed on those slow motion MBs with a small search range. The computation of IME is also highly reduced with this algorithm. Experimental results show that 61.4%-76.7% computation can be saved with the similar coding quality as the reference software. Moreover, the provided fast algorithms can be combined with fast block matching algorithms to further improve the performance.

The variable block size motion estimation (ME) and compensation (MC) using multiple reference frames is adopted in H.264/AVC to improve coding efficiency. However, the computational complexity for ME/MC increases proportional to the number of reference frames. In this paper, we propose a new efficient reference frame selection algorithm to reduce the complexity. The proposed algorithm selects suitable reference frames by employing the spatial and temporal correlation of video sequence. The experimental results show that the proposed algorithm decreases video encoding time while keeping the similar visual quality and bit rates.

The H.264 video compression standard uses enhanced Motion Estimation (ME) features to improve both the compression ratio and the quality of compressed video. The two primary enhancements are the use of Variable Block Size Motion Estimation (VBSME) and multiple reference frames. These two additions greatly increase the computational complexity of the ME algorithm, to the point where a software based real-time (30 frames per second (fps)) implementation is not possible on present microprocessors. Thus hardware acceleration of the H.264 ME algorithm is necessary in order to achieve real-time performance for the implementation of the VBSME and multiple reference frames features. This thesis presents a scalable FPGA-based ME architecture that supports real-time H.264 ME for a wide range of video resolutions ─ from 640x480 VGA to 1920x1088 High Definition (HD). The architecture contains innovations in both the data-path design and memory organization to achieve scalability and real-time performance on FPGAs. At 37% FPGA device utilization, the architecture is able to achieve 31 fps performance for encoding full 1920x1088 progressive HDTV video.

Motion Estimation is the most crucial and time-consuming part of the H.264/AVC video compression standard. The introduction of motion search of variable block sizes in multiple reference frames has significantly increased the computational complexity. This paper proposes a fast motion estimation algorithm, Most Used Reference First (MURF), based on the usage history of reference frames. The algorithm rearranges the search order of the reference frames based on the selection probability of the reference frames in coding the current frame. The experimental results show that the proposed algorithm, when compared to the best algorithm in H.264 reference software, achieves on average 60% reduction in search points and 52% reduction in motion estimation time with comparable video quality and negligible increase in bit-rate and memory.

In this paper, a new error concealment algorithm for the new coding standard H.264 is presented. The algorithm consists of a block size determination step to determine the size type of the lost block and a motion vector recovery step to find the lost motion vector from multiple reference frames. The main feature of this algorithm are as follows. In the block size determination step, we propose a criterion to determine the size type of the lost block from the current frame. In the motion vector recovery step, the optimal motion vector for the lost block chosen from multiple previous reference frames with the minimum value of the side match distortion. The proposed algorithm not only can determine the most correct mode for the lost block, but also can save much more computation time for motion vector recovery. Experimental results show that the proposed algorithm achieves 0.4/spl sim/7 dB improvement over the conventional VM method.

H.264IMPEG-4 AVC allows multiple reference frames and multiple partition types to improve the coding performance at the expense of much higher computation complexity. This paper proposes a fast motion estimation (ME) algorithm to reduce search complexity for this demanding configuration. Our experimental results show that the partition type which gives the lowest matching cost for the reference frame closest to the current picture in temporal domain is likely to be the best partition type for all reference frames. Based on this finding, the proposed method performs a less complicated search effort for the other partition types on the remaining reference frames to obtain significant savings in search complexity. This approach can be easily modified to support any existing fast search algorithms. Simulation results show the proposed algorithm significantly reduces the overall motion search complexity and at the same time, maintains minimum coding performance degradation.

The H.264 video coding standard adopts many new coding tools, such as variable block size, multiple reference frames, quarter-pixel-accuracy motion estimation, intra prediction, loop filter, etc. Using these coding tools, H.264 achieves significant performance. Especially for reference frame, block size, and predication direction, it can adaptively do the selection. However, the encoding complexity increases tremendously. Among these tools, the macro block modes selection and the motion estimation contributes most to total encoding complexity. This paper focuses on complexity reduction in macro block modes selection. On the basis of analyzing some fast mode selection algorithms, a new algorithm is presented, in order to reduce the time of encoding, in the precondition of reducing the quality of encoding obviously.

The H.264 video coding standard adopts different coding schemes like variable block sizes and multiple reference frames for motion estimation. Hence, H.264/AVC provides gains in compression efficiency of up to 50% over a wide range of bit rates and video resolutions compared to previous standards. However, these features incur a considerable increase in encoder complexity, mainly with regards to motion estimation and mode decision. In this paper, efficient methods aimed at speed up of motion estimation and mode decision are proposed. Experimental results show that our proposed algorithms can save a coding time of about 88% compared with the reference software JM80 while keeping the video quality.

To make full use of the temporal correlation of video sequences, H.264/AVC adopts multiple reference frames to enhance the coding quality. While the performance being improved, the complexity of computation has been increased linearly, too. In this paper, we study motion characteristic of video sequences and spatial correlation in video frames first, and then we propose an adaptive fast multiple reference frames selection algorithm. It can decrease the number of reference frames for motion compensation, and reduce the complexity of coding adaptively according to the features of video sequences. The results show that our algorithm achieves 45% coding time saving on average with unnoticeable quality loss.