Luong Pham Van

Fast transrating for high efficiency video coding based on machine learning

To incorporate the newly developed High Efficiency Video Coding (HEVC) standard in real-life network applications, efficient transrating algorithms are required. We propose a fast transrating scheme, based on the early prediction of the partition split-flags in P pictures. Using machine learning techniques, the correlation between co-located partitions at different quantizations is investigated. This results in a model which predicts the split-flag and gives the associated prediction accuracy so that the splitting process in the transcoder is optimized. At each partition depth, the model indicates whether the full rate-distortion cost evaluations should be performed at the current depth, or if the partition can be split immediately. Experimental results show that the proposed transcoder reduces the complexity of the transrating process by 76.04%, while maintaining the coding efficiency of a cascaded decoder-encoder.

Efficient Bit Rate Transcoding for High Efficiency Video Coding

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.

Fast motion estimation for closed-loop HEVC transrating

Transrating is a useful tool for adapting the bitrate of a video stream. Reducing the complexity of transrating is extremely important, especially for high efficiency video coding (HEVC) based transrating where the encoder is very time consuming. TZSearch is currently the default integer motion estimation (ME) algorithm in the HEVC reference encoder thanks to its excellent performance in reducing the complexity of ME. However, this algorithm is characterized by a fixed search area and search pattern, which can be considered sub optimal for transrating. In this paper, we improve the performance of HEVC transrating by optimizing this TZSearch algorithm. Utilizing the correlation between input and output motion vectors, we propose a fast search scheme including three steps. First, the initial search point is selected. Then, using the rate-distortion cost of this starting point, the search size is determined according to an online-trained Bayes decision rule. Finally, two proposed search algorithms are described for refining the starting point. Experimental results show that our proposed TZSearch scheme can reduce the complexity of transrating while improving the coding performance in terms of bit rate saving.

Machine learning for arbitrary downsizing of pre-encoded video in HEVC

In this paper, we propose a machine learning based transcoding scheme for arbitrarily downsizing a pre-encoded High Efficiency Video Coding video. The spatial scaling factor can be freely selected to adapt the output bit rate to the bandwidth of the network. Furthermore, machine learning techniques can exploit the correlation between input and output coding information to predict the split-flag of coding units in a P-frame. We analyzed the performance of both offline and online training in the learning phase of transcoding. The experimental results show that the proposed techniques significantly reduce the transcoding complexity and achieve trade-offs between coding performance and complexity. In addition, we demonstrate that online training performs better than offline training.

Performance analysis of machine learning for arbitrary downsizing of pre-encoded HEVC video

Nowadays, broadcasters deliver ultra-high resolution video to their consumers. This live video is sent to a set-top box for display on a television. However, if one or more users in the home want to view the same video on their personal mobile devices with a lower display resolution and limited processing power, decoding the original ultra-high resolution video would result in stuttering and quickly drain the battery life on these devices. To enable a satisfactory consumer experience, the resolution of the video stream should be adapted to the target mobile device at the set-top box. The aim of this paper is to investigate the performance of different machine learning strategies to arbitrary downsize video pre-encoded with the high efficiency video coding standard (HEVC). These machine learning techniques exploit correlation between input and output coding information to predict the splitting behavior of HEVC coding units. Several machine learning algorithms are optimized. Additionally, both online and offline training strategies are tested. Of the tested algorithms, online-trained random forests achieve the best compression-efficiency with a bit rate increase of 5.4% and an average complexity reduction of 70%1.

Adaptive deblocking filtering scheme for intra-coded slices in H.264/AVC

H.264/AVC applies an adaptive in-loop deblocking filter in order to remove blocking artifacts. The deblocking filter is adaptively controlled by using the boundary strength (BS) parameter. Although the filter achieves advantages in reducing blocking artifacts, there are two problems we should consider to improve the filtering performance. The first problem is that the filter applies just a strong and the strongest filter to filter intra coded blocks. The strong filter can remove blocking artifacts at smooth regions; however, it removes detail or sharpness of pictures at complex regions. The second problem is that H.264/AVC does not pay attention in removing corner outliers which appear when an edge of an object crosses a corner of a block. Therefore, corner outliers clearly appear and reduce the subjective quality of reconstructed pictures. In this paper, we propose an adaptive deblocking filter scheme which removes corner outliers, and which utilizes the intra prediction information of intra coded blocks to adaptively select the BS. Experimental results show that our proposed filter significantly enhances the subjective quality by removing blocking artifacts at smooth regions while maintaining details or sharpness at complex regions. In addition, the objective quality is improved, resulting in PSNR gains of up to 0.16 dB.

An OFDM System Design with Efficient ICI Cancellation over Embedded LTE-R Channels Based on the USRP Platform

In this paper, we design an OFDM communication system with ICI cancellation embedded over LTE-R channel. In our proposed system, a very fast time-varying multipath fading channel is integrated with the use of D2a scenario of WINNER II channel model. The ICI cancellation approaches, wherein a PSK modulated symbol is mapped onto multiple subcarriers, are investigated and deployed in the Universal Software Radio Peripheral platform as the RF front-end. Experimental results confirm that the ICI cancellation techniques offer a notably high performance on eliminating the problem caused by frequency offsets. For instance, the ICI cancellation solutions achieve the same performance at Doppler frequency of 200 Hz compared to the system without using our approaches at a lower Doppler of 50 Hz.

Intra-frame sharing for low-complexity decoding of SHVC video

Scalable video coding provides an efficient adaptation solution for delivering video to consumer devices with a great variety of characteristics. This technique encodes video using a base layer and several enhancement layers with different qualities. In order to reduce the total bit rate of the video stream, inter layer prediction (ILP) is enabled. However, the introduction of ILP results in a high decoding complexity since multiple layers need to be decoded at the same time. In this paper, we propose a method to reduce the decoding complexity of the scalable video stream by sharing the intra frame between layers and eliminating all other inter-layer dependencies. The experimental results show that the proposed method results in a video stream with a lower bit rate than a simulcast approach. Additionally, this approach reduces the decoder complexity compared to scalable video compression.

Out-of-the-loop information hiding for HEVC video

Communication using internet and digital media is more and more popular. Therefore, the security and privacy of data transmission are highly demanded. One effective technique providing this requirement is information hiding. This technique allows to conceal secret information into a video file, an audio, or a picture. In this paper, we propose a low complexity out-of-the-loop information hiding algorithm for a video pre-encoded with the high efficiency video coding standard. Only selected components such as the motion vector difference and transform coefficients of the video are extracted and modified, bypassing the need of fully decoding and re-encoding the video. In order to reduce the propagation error caused by hiding information, the dependency between video frames is taken into account when distributing the information over the frame. Several embedding strategies are investigated. The experimental results show that the information should be hidden in smaller blocks to reduce quality loss. Using a smart distribution of information across the frames can keep the quality loss under 1 dB PSNR for an information payload of 15 kbps. When such a strategy is used, embedding information in the transform coefficients only slightly outperforms the modification of motion vector differences.

Complexity Reduction of IDCT by using CBP and Adaptive Pruning in H.264/AVC Decoder

In this paper, we propose two methods to reduce computational complexity of H.264/AVC decoder. The proposed methods utilize CBP (Coded Block Pattern) information to skip inverse DCT transform and reconstruction processes for 8x8 blocks that have CBP bits being zero. Furthermore, we adaptively skip zero coefficients in a block to reduce unnecessary computation in inverse DCT transform module. Experimental results show that the proposed method can reduce decoding complexity by about 10 % with only a negligible PSNR drop.