Fast Inter-prediction Research Articles

Exploiting Data Mining for Fast Inter Prediction Mode Decision in HEVC

HEVC has made significant progress in coding efficiency. Compared with the previous generation H.264/AVC video coding standard, HEVC can save 50% of code rate under the same visual quality. However, this drop in bit rate is accompanied by an increase in computational complexity, which poses a great challenge to the application and development of HEVC. After analyzing the great correlation between the partitioning mode of coding unit in the encoder and several parameters, the partitioning problem of the CU is modeled as a classification problem in data mining, and it is decided to use a decision tree to solve this classification problem. We implement the decision tree on the official software HM16.2 and test the algorithm on the test set. Experiments show that fast algorithm based on decision tree can improve the encoding speed effectively with little influence on the encoding performance.

Fast inter-prediction algorithm based on motion vector information for high efficiency video coding

High Efficiency Video Coding (HEVC/H.265) is the latest international video coding standard, which achieves better compression ratio and supports higher resolution than Advanced Video Coding (H.264/AVC). However, HEVC/H.265 increases the computational burden. To reduce the coding complexity of the HEVC encoder, this paper proposes a fast inter-prediction algorithm to speed up coding time. We collect the average rate-distortion costs (RD-cost) of Skip modes and Merge modes to accelerate prediction unit (PU) mode decisions. In addition, we also acquire and analyze the motion vector range from Merge modes and Inter 2N × 2N modes to decide whether to execute Merge and advanced motion vector prediction (AMVP) of other PUs. The experimental results show that the proposed algorithm provides 48.54% time saving on average in random-access configuration and maintains good rate-distortion performance and video quality at the same time. The proposed algorithm also outperforms previous works.

Fast inter-prediction algorithms for spatial Scalable High efficiency Video Coding SHVC

Scalable High Efficiency Video Coding (SHVC) has aggregated an exhaustive algorithm for mode decision based on a recursive quad-tree structure coding unit. However, this comes at the cost of high computational complexity. Therefore, in order to improve the SHVC in terms of encoding time, fast inter-mode decision algorithms for spatial scalability were suggested in this paper. This study focuses on using statistical analysis and the correlation between base layer and enhanced layer modes to speed up the mode decision algorithm for inter-mode. Consequently, the proposed method concerns only the inter-prediction module. It is devoted to three principal levels: CUs level, PUs, and skip mode. Experimental results for different proposed methods showed an important improvement in terms of encoding time which is the most important constraint for any real-time implementation. Moreover, the combination of these three methods could achieve up to 48% of time reduction with insignificant loss in video quality.

A Fast Inter Prediction Method in HEVC Using SAD Computing Algorithm Based on Bottom-up Design

A Fast Inter Prediction Method in HEVC Using SAD Computing Algorithm Based on Bottom-up Design

Fast inter-prediction mode decision algorithm for HEVC

The high efficiency video coding (HEVC) is superior to the previous video coding standards in compression performance, while the computational complexity is introduced simultaneously. The complexity increases mainly due to the novel flexible partitioning scheme that allows the inter-prediction mode partition split via exhaustive rate-distortion optimization (RDO). In this paper, a fast inter-prediction mode decision algorithm is proposed, which contains adaptive threshold determination based on quantization parameter and fast inter-prediction mode partition decision. The proposed algorithm utilizes the edge information of the partition to simplify the RDO and then accelerates coding time of inter-prediction mode decision structure for the original HEVC encoder. The experimental results show that the proposed algorithm can achieve 39.5 % coding time reduction with just 1.97 % bitrate increase on average under random access condition and 35.2 % coding time reduction with just 1.89 % bitrate increase on average under low-delay B condition, compared to the original HEVC encoder.

A Fast Inter Prediction Algorithm Based on Rate-Distortion Cost in HEVC

A Fast Inter Prediction Algorithm Based on Rate-Distortion Cost in HEVC

Implementation and Optimization of A Fast Inter Prediction Algorithm

Audio Video coding Standard is the second generation Source Coding-Decoding standards of China, especially for embedded audio/video platform. This paper proposes an efficient and fast inter prediction algorithm, which is one of the key techniques of Audio Video coding Standard.Reducing of the redundancy in source sequence inter prediction, it could improve the picture quality. The optimization schemes include two aspects,which are the algorithm framework, variables and data structure. Optimized results demonstrate that our algorithm has a notable improvement of the clockcycle efficiency. Furthermore, this research also gives a valuable insight of the combination with quantum information. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.2992 Full Text: PDF

An Efficient Inter-Prediction Mode Decision Method for Temporal Scalability Coding With Hierarchical B-Picture Structure

Although the temporal scalability coding supported by both H.264/AVC and H.264/SVC provides a good means of adapting bitstream scalability in temporal dimension, it entails heavy computational complexity due to bidirectional motion estimation and compensation for B-pictures in temporal scalability levels. To make it more applicable, this paper introduces a fast inter-prediction mode decision method which is efficiently performed using statistical hypothesis testing. The hypothesis testing is performed on mean and variance of pixels in 16 × 16 and 8 × 8 blocks to decide early termination against further processing for their sub-blocks. Fallback check is employed to compromise the tradeoff between the compression efficiency and encoding time saving. The proposed method exhibits effective performance for early termination in the inter-prediction mode decision, thus leading to a significant reduction of the total encoding time up to average 64.8% with small increments in bit amounts and small degradation in visual quality.

Cascaded Direction Filtering for Fast Multidirectional Inter-Prediction in H.264/AVC Main and High Profile Compression

Early direct mode decision is a popular technique to improve the execution speed of inter-predictions in a B slice. However, the improvement is limited when this technique is applied to a hardware-based pipelined architecture or to the video sequences where the ratio of macroblocks (MBs) encoded as the direct mode is low. This paper proposes a novel fast inter-prediction algorithm for B slices which increases the encoding speed by early decision of the direction of the inter-prediction using spatial and temporal correlation in a video. A speed-up is achieved by selecting a prediction direction with a lower complexity and discarding a prediction direction with a higher complexity. For the case when early mode decision is not possible, additional speed-up is achieved by partially skipping unidirectional motion estimation (ME) if the estimated cost of the ME is greater than the result of the direct mode prediction that is completed much faster than ME. This proposed sequence of early decisions is referred to as cascaded direction filtering (CDF). The proposed algorithm increases the early mode decision rate, which improves the encoding speed effectively for a hardware-based pipelined architecture as well as videos with a low ratio of MBs encoded as the direct mode. The additional memory size and bandwidth overhead required for the proposed CDF are very small. Experimental results show that the proposed CDF scheme improves the encoding speed by 45% for B slices and 32% for overall sequences. The bitrate is increased by less than 1% with a small peak signal-to-noise ratio degradation of 0.03 dB on average.

Divide sampling–based hybrid temporal-spatial prediction coding for H.264/AVC

A divide sampling-based hybrid temporal-spatial prediction coding algorithm is designed to further improve the coding performance of the conventional H.264/AVC coding. In the proposed algorithm, a frame is first divided into four equal-sized subframes, and the first subframe is coded using the rate distortion optimization model with inter- or intraprediction adaptively. Then, the optimal prediction method of the macroblock in other subframes is selected flexibly and reasonably from intraprediction, the fast interprediction, and the spatial interpolation prediction. The simulation results show that compared with the conventional H.264/AVC coding, the average bit rate is reduced by 6.15% under the same peak signal-to-noise ratio (PSNR), the average PSNR is increased by 0.22 dB under the same bit rate, and the average coding time is saved by 12.40% in the proposed algorithm.

Adaptive search range decision for fast intra-and inter-prediction mode decision in H.264/AVC

Adaptive search range decision for fast intra-and inter-prediction mode decision in H.264/AVC

An MPEG-2 to H.264 Video Transcoder in the Baseline Profile

Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format and the distribution of the motion compensated residual in MPEG-2. Moreover, a dynamic motion estimation technique is also proposed to further speed-up the decision process. Finally, we go a step further on our previous research efforts by combining the two aforementioned speed-up approaches. Our simulation results over more than 40 sequences at common intermediate format and quarter common intermediate format resolutions show that our proposal outperforms the MB mode selection of the rate-distortion optimization option of the H.264 encoder process by reducing the computational requirements by up to 90%, while maintaining the same coding efficiency. Finally, we conduct a comparative study of our approach with the most relevant fast inter-prediction methods for MPEG-2 to H.264 transcoder recently reported in the literature.

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.

Fast motion search with efficient inter-prediction mode decision for H.264

A fast inter-prediction mode decision and motion search algorithm is proposed for the H.264 video coding standard. The multi-resolution motion estimation scheme and an adaptive rate-distortion model are employed with early termination rules to accelerate the search process. With the new algorithm, the amount of computation involved in the motion search can be substantially reduced. Experimental results show that the proposed algorithm can achieve a speed-up factor ranging from 60 to 150 times as compared to the full-search algorithm with little quality degradation.