Encoding Time Research Articles

Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency

Quasi-cyclic low-density parity-check (QC–LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.

Sleep deprivation impairs binding of information with its context.

Binding information to its context in long-term memory is critical for many tasks, including memory tasks and decision making. Failure to associate information to its context could be an important aspect of sleep deprivation effects on cognition, but little is known about binding problems from being sleep-deprived at the time of encoding. We studied how sleep deprivation affects binding using a well-established paradigm testing the ability to remember auditorily presented words (items) and their speakers (source context). In a laboratory study, 68 healthy young adults were randomly assigned to total sleep deprivation or a well-rested control condition. Participants completed an affective item and source memory task twice: once after 7-hour awake during baseline and again 24 hours later, after nearly 31 hours awake in the total sleep deprivation condition or 7 hours awake in the control condition. Participants listened to negative, positive, and neutral words presented by a male or female speaker and were immediately tested for recognition of the words and their respective speakers. Recognition of items declined during sleep deprivation, but even when items were recognized accurately, recognition of their associated sources also declined. Negative items were less bound with their sources than positive or neutral items, but sleep deprivation did not significantly affect this pattern. Our findings indicate that learning while sleep-deprived disrupts the binding of information to its context independent of item valence. Such binding failures may contribute to sleep deprivation effects on tasks requiring the ability to bind new information together in memory.

Light weight model for intra mode selection in HEVC

The High Efficiency Video Coding (HEVC) efficiently reduces the size of the multimedia contents, but at the cost of high computation complexity. In order to make it work for the real time applications, a study is conducted in this article to speed up the intra mode decision of HEVC. Firstly, a novel early termination mechanism is proposed that is based on the duration problem. This model is designed such that it requires the least amount of computation and dynamically adjusts to the current best option. Secondly, a dynamic threshold is proposed that outperformed majority of the thresholds in-terms of computation and adjustment with-respect-to the current best option. Experimental results proved that when the proposed methods are incorporated in HEVC, they reduced the encoding time by 34.1% on average, with a minor bit-rate (BD-BR) increase of 1.4% on average.

A Tamper-Resistant Algorithm Using Blockchain for the Digital Tachograph

A tachograph in a vehicle records the vehicle operating conditions, such as speed, distance, brake operation conditions, acceleration, GPS information, etc., in intervals of one second. For accidents, the tachograph records information, such as the acceleration and direction of a vehicle traveling in intervals of 1/100 s for 10 s before and after the accident occurs as collision data. A vehicle equipped with a tachograph is obliged to upload operation data to administrative organizations periodically via other auxiliary storage devices like a USB attached external memory or online wireless communication. If there is a problem with the recorded contents, data may be at risk of being tampered with during the uploading process. This research proposed tamper-resistant technology based on blockchain for data in online and offline environments. The suggested algorithm proposed a new data recording mechanism that operates in low-level hardware of digital tachographs for tamper-resistance in light blockchains and on/offline situations. The average encoding time of the proposed light blockchain was 1.85 ms/Mb, while the average decoding time was 1.65 ms/Mb. With the outliers in statistical tests removed, the estimated average encoding and decoding time was 1.32 ms/Mb and 1.29 ms/Mb, respectively, and the tamper verification test detected all the tampered data.

Co‐design implementation of High Efficiency Video Coding standard encoder on Zynq MPSoC

SummaryStatistical analysis of High Efficiency Video Coding (HEVC) encoder reveals that in the motion compensation block, the interpolation filter consumes more than 30% in the encoder time in comparison with other blocks. In this paper, we start with an optimized hardware implementation of the interpolation filter on field‐programmable gate array (FPGA) based on Xilinx setup environment. In a second step, a Hardware/Software (HW/SW) co‐design implementation of HM16.7 encoder is performed on Zynq MPSoC platform to evaluate the proposed interpolation filter IP in terms of total encoder run‐time, taking advantages of both processing units (quad‐core ARM Cortex TM‐A53 processor and Programmable Logic FPGA component) available on the Zynq MPSoC. The proposed architecture of luma and chroma filters was simulated and synthesized on Xilinx XCZU7EV‐2FFVC1156 FPGA at 250‐MHz clock frequency. The synthesis results present an optimized power consumption of 3.308 mW for higher resolutions (2560 × 1600 and 1920 × 1080) at 50 fps with the use of just 1% of the FPGA resources. The experimental results of the co‐design implementation of HEVC encoder present a speedup of 2 times (41% in PeopleOnStreet sequence) in terms of processing time compared to the software alone implementation, with a an increase of 0.51% of bit rate and a very small degradation of peak signal‐to‐noise ratio (PSNR) (0.01%).

Ultrashort Echo Time Magnetic Resonance Angiography in Follow-up of Intracranial Aneurysms Treated With Endovascular Coiling: Comparison of Time-of-Flight, Pointwise Encoding Time Reduction With Radial Acquisition, and Contrast-Enhanced Magnetic Resonance Angiography.

The optimal magnetic resonance angiography (MRA) sequence for assessing the aneurysm occlusion state or in-stent flow after endovascular coiling is not well established. To evaluate the diagnostic performance of pointwise encoding time reduction with radial acquisition (PETRA)-MRA in patients who underwent endovascular coiling relative to that of time-of-flight (TOF)-MRA and contrast-enhanced (CE)-MRA. We evaluated the aneurysm occlusion state using digital subtraction angiography (DSA) and MRA. In patients who underwent stent-assisted coiling, we estimated the visibility of in-stent flow. We enrolled 189 patients with assessable TOF, PETRA, and CE-MRAs after coiling. In patients who underwent simple coiling (128 patients), PETRA showed a higher sensitivity in the detection of residual flow than TOF and CE (PETRA, 100%; CE, 83%; TOF, 80%). There were no significant differences in the height of residual flow between DSA (0.68± 1.45mm) and PETRA (0.70± 1.50mm; P=1.000). In patients who underwent stent-assisted coiling (61 patients), PETRA showed the highest sensitivity (88%) in detecting residual flow (CE, 56%; TOF, 31%). Regarding in-stent flow, PETRA, CE, and TOF showed visual scores of≥3 with frequencies of 96.7%, 85.2%, and 37.7%, respectively. Relative signal-to-noise ratio of PETRA (0.62± 0.18) was significantly higher than that of CE (0.56± 0.12) and TOF (0.39± 0.12; P <.001 for both). PETRA-MRA showed excellent diagnostic performance in terms of residual flow detection and in-stent flow assessment. PETRA could be a versatile alternative sequence for following up patients with coiled aneurysm.

Performance Analysis of LDPC Decoding Techniques

Low density parity checking codes (LDPC) are one of the most important issues in coding theory at present. LDPC-code are a type of linear-block LDPC-codes. Channel coding might be considered as the finest conversant and most potent components of cellular communications systems, that was employed for transmitting errors corrections imposed by noise, fading and interfering. LDPC-codes are advanced coding gain, i.e., new area in coding. the performances of LDPC-code are similar to the Shannon-limiting, this led to the usage of decoding in several applications in digital communications systems, like DVB-S2 and WLAN802.1..This paper aims to know what is LDPC,what its application and introduce encoding algorithms that gives rise to a linear encoding time and also show that the regular and irregular LDPC performance and also introduce different methods for decoding LDPC. I discuss in detail LDPC decoding algorithm: bit flipping algorithm, as a type from hard decision .belief propagation algorithm, sum product algorithm and minimum sum algorithm as examples from soft decision .I expect that at least some students or researchers involved in researching LDPC codes would find this paper helpful.

Explicit List-Decodable Codes with Optimal Rate for Computationally Bounded Channels

A stochastic code is a pair of encoding and decoding procedures (Enc, Dec) where \({{\rm Enc} : \{0, 1\}^{k} \times \{0, 1\}^{d} \rightarrow \{0, 1\}^{n}}\). The code is (p, L)-list decodable against a class \(\mathcal{C}\) of “channel functions” \(C : \{0,1\}^{n} \rightarrow \{0,1\}^{n}\) if for every message \(m \in \{0,1\}^{k}\) and every channel \(C \in \mathcal{C}\) that induces at most pn errors, applying Dec on the “received word” C(Enc(m,S)) produces a list of at most L messages that contain m with high probability over the choice of uniform \(S \leftarrow \{0, 1\}^{d}\). Note that both the channel C and the decoding algorithm Dec do not receive the random variable S, when attempting to decode. The rate of a code is \(R = k/n\), and a code is explicit if Enc, Dec run in time poly(n).Guruswami and Smith (Journal of the ACM, 2016) showed that for every constants \(0 < p < \frac{1}{2}, \epsilon > 0\) and \(c > 1\) there exist a constant L and a Monte Carlo explicit constructions of stochastic codes with rate \(R \geq 1-H(p) - \epsilon\) that are (p, L)-list decodable for size \(n^c\) channels. Here, Monte Carlo means that the encoding and decoding need to share a public uniformly chosen \({\rm poly}(n^c)\) bit string Y, and the constructed stochastic code is (p, L)-list decodable with high probability over the choice of Y.Guruswami and Smith pose an open problem to give fully explicit (that is not Monte Carlo) explicit codes with the same parameters, under hardness assumptions. In this paper, we resolve this open problem, using a minimal assumption: the existence of poly-time computable pseudorandom generators for small circuits, which follows from standard complexity assumptions by Impagliazzo and Wigderson (STOC 97).Guruswami and Smith also asked to give a fully explicit unconditional constructions with the same parameters against \(O(\log n)\)-space online channels. (These are channels that have space \(O(\log n)\) and are allowed to read the input codeword in one pass.) We also resolve this open problem.Finally, we consider a tighter notion of explicitness, in which the running time of encoding and list-decoding algorithms does not increase, when increasing the complexity of the channel. We give explicit constructions (with rate approaching \(1 - H(p)\) for every \(p \leq p_{0}\) for some \(p_{0} >0\) ) for channels that are circuits of size \(2^{n^{\Omega(1/d)}}\) and depth d. Here, the running time of encoding and decoding is a polynomial that does not depend on the depth of the circuit.Our approach builds on the machinery developed by Guruswami and Smith, replacing some probabilistic arguments with explicit constructions. We also present a simplified and general approach that makes the reductions in the proof more efficient, so that we can handle weak classes of channels.

Hi-def memories of lo-def scenes.

The study of visual memory is typically concerned with an image's content: How well, and with what precision, we can recall which objects, people, or features we have seen in the past. But images also vary in their quality: The same object or scene may appear in an image that is sharp and highly resolved, or it may appear in an image that is blurry and faded. How do we remember those properties? Here six experiments demonstrate a new phenomenon of "vividness extension": a tendency to (mis)remember images as though they are "enhanced" versions of themselves -that is, sharper and higher quality than they actually appeared at the time of encoding. Subjects briefly saw images of scenes that varied in how blurry they were, and then adjusted a new image to be as blurry as the original. Unlike an old photograph that fades and blurs, subjects misremembered scenes as more vivid (i.e., less blurry) than those scenes had actually appeared moments earlier. Follow-up experiments extended this phenomenon to saturation and pixelation - with subjects recalling scenes as more colorful and resolved - and ruled out various forms of response bias. We suggest that memory misrepresents the quality of what we have seen, such that the world is remembered as more vivid than it is.

Fast SHVC intra prediction mode decision implementation

The Scalable extension of the High Efficiency Video Coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC with the possibility of encoding several different versions of the same encoded video in a single bitstream. However, this comes at the cost of a high computational complexity. In order to reduce the SHVC encoding time, an intra-prediction mode decision algorithm is proposed. The approach is based on a statistical study on different type video sequences to promote most used intra prediction modes. For each block size, the proposed algorithm defines possible coding modes in the rate-distortion process. The proposed algorithm was validated on two different architectures: first on a mono-core processor and then on a multi-core DSP. The restriction of the intra prediction mode allowed an important reduction of processing time by 78.25% leading to a reduction in the total encoding time by 15.898% on average. About the video quality, a merely 0.043 dB PSNR loss resulting with a 3.404% bit rate increase.

Machine learning-based fast CU size decision algorithm for 3D-HEVC inter-coding

3D-high efficiency video coding (3D-HEVC) is an extension of the high efficiency video coding (HEVC) standard for the compression of the texture videos and depth maps. In 3D-HEVC inter-coding, the coding unit (CU) is recursively performed on variable sizes, namely, depth levels. The CU size decision process is conducted using all the possible depth levels to obtain the one with the least rate-distortion (RD) cost using the Lagrange multiplier. These tools achieve the highest coding efficiency but incur a very high computational complexity. In this paper, a fast CU size decision algorithm is proposed to reduce the complexity caused by the CU size splitting process. The proposed algorithm is based on the CU homogeneity classification using machine learning technology. First, the tensor feature is extracted to characterize the homogeneity of CU, which has a strong relationship with CU sizes. Then, a boosted decision stump algorithm is employed to analyze and construct a binary classification model from the extracted features and find suitable thresholds for the proposed method. Finally, an efficient early termination of CU splitting is released based on adaptive thresholds for texture videos and depth maps. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.

Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning

HTTP Adaptive Streaming (HAS) is the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08% and parallel encoding time by 41.26%, with a 0.89% bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27% for the overall encoding and 27.71% for the parallel encoding on average with a 2.05% bitrate increase.

FAST FRACTAL IMAGE ENCODER DESIGN

Fractal theory has been widely applied in the filed of image compression due to the advantage of resolution independence, fast decoding, and high compression ratio. However, it has a fatal shortcoming of intolerant encoding time because that every range block is need to find its corresponding best matched domain block in the full image. Therefore, it has not been widely applied as other coding schemes in the field of image compression. In this paper, an algorithm is proposed to improve this time-consuming encoding drawback by the adaptive searching window, partial distortion elimination and characteristic exclusion algorithms. Proposed can efficient decrease the encoding time significantly. In addition, the compression ratio is also raised due to the reduced searching window. Conventional fractal encoding for a 512 by 512 image need search 247009 domain blocks for every range block. Experimental results show that our proposed method only search 120 domain blocks which is only 0.04858% compared to conventional fractal encoder for every range block to encode Lena 512 by 512 8-bit gray image at the bit rate of 0.2706 bits per pixel (bpp) while maintaining almost the same decoded quality as conventional fractal encoder does. This paper contributes to the research of encoder of fast image communication system.

Fast CU Partition Decision Algorithm for AVS3 Intra Coding

The third generation of Audio Video coding Standard (AVS3) is an emerging video coding standard that surpasses High Efficiency Video Coding (HEVC). AVS3 allows flexible block subdivision by applying Quad-Tree (QT), Binary-Tree (BT) plus Extend Quad-Tree (EQT) partition structure, while the increased flexibility comes at the cost of enormous coding complexity. In this paper, an ingenious early termination mechanism is proposed to skip unnecessary exhaustive searches of the whole tree branches. We carry out a series of restrictive measures based on Coding Unit (CU) size and iteration status to make the sophisticated EQT split mode concentrate more on small CUs with complex texture structure. Historical QTBT partition information is also adopted as an important factor to skip EQT split mode in advance. Meanwhile, a fast CU partition algorithm based on the gradient is proposed to skip horizontal or vertical BT/EQT partition of CUs with prominent texture structure in another direction, in which early termination can be directly conducted in homogenous areas at the same time. Extensive experiments demonstrate that the proposed method can save 43% encoding time with only 0.53% BDBR increase on average under All Intra(AI) configuration, which outperforms the preexisting fast algorithms.

Fast Screen Content Coding in HEVC Using Machine Learning

Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new tools such as Intra Block Copy (IBC) and Palette (PLT) mode. These tools improve the coding efficiency, but they come with a huge computation complexity. In this paper, we propose a scheme to reduce the encoding time of SC encoder. It has two algorithms based on the Decision Rule (DR) machine learning technique. The first algorithm, which is called Mode Skipping (MS), is used to skip the unnecessary SC mode checking. Early Pruning Termination (EPT), which is the second algorithm, is used to stop the partitioning process early. A small number of features has to be calculated for the trained models. The proposed scheme was implemented then simulated by using the standard software test model SCM-6. The experimental results show that the time complexity is reduced by 37.84% on average while the Bjontegaard delta bit-rate (BD-R) increases by 1.34% only.

Efficient High-Resolution Video Compression Scheme Using Background and Foreground Layers

Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. HoangVan [18].

Hardware Implementation for the HEVC Fractional Motion Estimation Targeting Real-Time and Low-Energy

This paper presents an energy-aware and high-throughput hardware design for the Fractional Motion Estimation (FME) compliant with the High Efficiency Video Coding (HEVC) standard. An extensive software evaluation was performed to guide the hardware design. The adopted strategy mainly consists in using only the four squareshaped Prediction Unit (PU) sizes rather than using all 24 possible PU sizes in the Motion Estimation (ME). This approach reduces about 59% the total encoding time and, as a penalty, it leads to an increase of only 4% in the bit rate for the same image quality. Together with this simplification, a multiplierless approach, algebraic optimizations and low-power techniques were applied to the hardware design to reduce the hardware-resource usage and the energy consumption, maintaining a high processing rate. The architecture was described in VHDL and the synthesis results for ASIC 45nm Nangate standard cells demonstrate that the developed architecture is able to process Ultra-High Definition (UHD) 2160p videos at 60 frames per second (fps), with the lowest power consumption and the lowest hardware-resource usage among the related works.

Memory-aware Workload Balancing Technique based on Decision Trees for Parallel HEVC Video Coding

Video coding applications demand high computational effort to achieve high compression rates at a low perceptual quality expense. In order to reach acceptable encoding time for such applications, modern video coding standards have been em-ploying parallelism approaches to exploit multiprocessing plat-forms, such as the tiling tool from HEVC standard. When employing Tiles, each frame is divided into rectangular-shaped regions which can be encoded independently. However, alt-hough it is possible to distribute the data equally among the processing units when using Tiles, balancing the workload among processing units poses great challenges. Therefore, this paper proposes a workload balancing technique aiming to speed up the HEVC parallel encoding using Tiles. Different from other literature works, the proposed solution uses a novel approach employing static uniform tiling to avoid memory management difficulties that may emerge when dynamic tiling solutions are employed. The proposed technique relies on workload distribution history of previous frames to predict the workload distribution of the current frame. Then, the pro-posed technique balances the workload among Tiles by em-ploying a workload reduction scheme based on decision trees in the coding process. Experimental tests show that the pro-posed solution outperforms the standard uniform tiling and it is competitive with related works in terms of speedup. Moreo-ver, the solution optimizes resources usage in multiprocessing platforms, presents a negligible coding efficiency loss and avoids increasing memory bandwidth usage by 9.8%, on aver-age, when compared to dynamic tiling solutions, which can impact significantly the performance in memory-constrained platforms.

Fast Intra Mode Decision Algorithm for HEVC Based on Block Textural Properties

In order to achieve greater coding efficiency compared with the previous video coding standards, various advanced coding techniques are used in the High Efficiency Video Coding (HEVC) standard, such as a flexible partition and a large number of intra prediction modes. However, these techniques lead to much greater complexity that restricts HEVC from realtime applications. To solve this problem, a fast intra mode decision algorithm is proposed in this paper that uses the block’s textural properties to determine the partition depth range and decide whether to split or skip smaller sizes of the coding unit. Besides that, the number of candidate modes for the rough mode decision process is also reduced depending on the block’s property. Experimental results for the recommended test sequences by the JCT-VC show that the proposed algorithm can save an average of 44% encoder time with a slight loss in performance compared to the reference software HM-16.20.

Toward nonparametric diffusion- characterization of crossing fibers in the human brain.

PurposeTo estimate T1 for each distinct fiber population within voxels containing multiple brain tissue types.MethodsA diffusion‐T1 correlation experiment was carried out in an in vivo human brain using tensor‐valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P(D,R1) of diffusion tensors and longitudinal relaxation rates R1=1/T1. Orientation distribution functions (ODFs) of the highly anisotropic components of P(D,R1) were defined to visualize orientation‐specific diffusion‐relaxation properties. Finally, Monte Carlo density‐peak clustering (MC‐DPC) was performed to quantify fiber‐specific features and investigate microstructural differences between white matter fiber bundles.ResultsParameter maps corresponding to P(D,R1)’s statistical descriptors were obtained, exhibiting the expected R1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R1 between major crossing fiber bundles. These differences, confirmed by MC‐DPC, were in qualitative agreement with previous model‐based works but seem biased by the limitations of our current experimental setup.ConclusionsOur Monte Carlo framework enables the nonparametric estimation of fiber‐specific diffusion‐T1 features, thereby showing potential for characterizing developmental or pathological changes in T1 within a given fiber bundle, and for investigating interbundle T1 differences.