Coding Unit Research Articles

VERBALIZATION OF THE ENGLISH CONCEPT GENIUS IN APHORISMS

The article is dedicated to the investigation of the peculiarities of verbalization of the concept GENIUS in the English language, namely, consideration of the aphorisms with the component genius. According to the representatives of the semantic-cognitive approach, concept is a basic unit of human mental code, which has an internal structure consisting of conceptual features; it is the result of the individual and social learning of the world. Conceptual features contain comprehensive information about the corresponding object or phenomenon, as well as the interpretation of information of public consciousness and the treatment of the subject or phenomenon. Thus, the representatives of this trend treat language as one of the main tools of cognition and conceptualization of the world. The study of concept through the language is the most reliable way of linguistic analysis which allows to detect conceptual features and to work out the model of the concept. Any analysis of the concept starts by detecting language units that represent a given concept, the interpretation of their vocabulary and speech contexts. Aphorism is defined as a short original statement, a generalized, deep thought, expressed in a concise form, sometimes unexpectedly paradoxical. During the study, 100 English aphorisms with the genius component have been analyzed. It has been established that in the given expressions several models of the conceptual metaphor are realized. Conceptual metaphor is the main means of the conceptual system of a person by which we understand and perceive one type of objects in terms of objects of another type. The research made it possible to distinguish several conceptual metaphors, in particular GENIUS IS MADNESS, GENIUS IS FIRE, GENIUS IS WORK, GENIUS IS PRECIOUS, GENIUS IS ENEMY, GENIUS IS SIMPLE.

A Fast Intra Mode Selection Algorithm Based on CU Size for Virtual Reality 360∘ Video

Virtual Reality (VR) 360∘ video is spherical, and has ultra-high definition. It is time-consuming to code it using High Efficient Video Coding (HEVC) directly. In this paper, a fast intra mode selection algorithm based on Coding Unit (CU) size is proposed for VR 360∘ video in Cubemap projection (CMP) format. A redefined mode selection strategy for two-stage Rough mode decision (RMD) is designed based on different depth CUs to reduce coding time during intra prediction process. The experimental results show that the proposed algorithm brings 27.70% saving in average coding time at the cost of only 0.8% Bjontegaard delta rate increase in All-Intra mode.

Hybrid stopping model-based fast PU and CU decision for 3D-HEVC texture coding

As an extension of High-Efficiency Video Coding (HEVC) standard, 3D-HEVC needs to encode multiple texture views and depth maps, which further increases the computational complexity. To reduce the complexity of dependent texture view coding, a fast prediction unit (PU) and coding unit (CU) decision method is proposed for 3D-HEVC based on hybrid stopping model. The inter-view correlation is used as a priori information to roughly predict the possible optimal PU and CU sizes. Then, by exploiting the encoded posterior information, the rate distortion cost correlation and the code block flag, the optimal PU and CU are further examined as being optimal or not. Experimental results show that the proposed fast PU and CU decision method achieves 52.7% encoding time saving on average with negligible loss of coding efficiency for 3D-HEVC-dependent texture view coding.

Fast CU Depth Decision for HEVC Using Neural Networks

This paper presents a coding unit (CU) depth-decision algorithm using neural networks to reduce the computational overhead of High Efficiency Video Coding (HEVC). The coding tree unit (CTU) of HEVC has a quad-tree structure, and its computational complexity is considerably high because it searches an optimal CU depth from the upper to lower depth recursively and exhaustively. In the proposed method, neural networks are used to predict the CTU depth. A database for neural networks is constructed, which considers both the image and encoding properties of the CU. It consists of the image data representing the image value of the CU, the vector data based on the encoding information of the CU, and the labels indicating whether the CU is divided. By using both properties of the CU, high test accuracy can be achieved. It is completely separated from the test sequence used for encoding and can be configured to use a sequence with various resolutions, motions, and contents for diverse CUs. We also design a neural-network architecture and perform training. The architecture consists of the convolution and pooling layers for analyzing the image property of the CU. The feature map is concatenated with the vector data and trained by fully connected layers in order to analyze the encoding property of the CU. Finally, a fast CU depth-decision algorithm is designed based on the trained neural networks. When the result of the neural network inference with the current CU depth is non-split, the operation on the lower CU depth is skipped. The experimental results show that the proposed method can reduce the computational overhead by 61.77% on average, and by a maximum of 73.45% with 3.91% Bjøntegaard-Difference-bitrate (BD rate) degradation.

A Motion-Based Partitioning Algorithm for HEVC Using a Pre-Analysis Stage

The High Efficiency Video Coding (HEVC) standard has shown large improvements in coding efficiency compared with previous standards. In particular, HEVC outperforms H.264/MPEG-4 Advanced Video Coding by up to 50% in terms of bitrate reduction for similar perceptual quality. This improvement is the result of the introduction of new coding tools, which enable the representation of data using fewer bits, but at the cost of long computation times. One of the most significant tools introduced by HEVC is the novel quadtree-based structure called the coding tree unit (CTU), which can be subsequently split into coding units (CUs), prediction units (PUs), and transform units (TUs), providing huge flexibility in the encoding. However, selecting the optimal tree partitioning requires the evaluation of a huge number of possibilities, which constitutes the most complex operation for the encoder. In order to tackle this task, this paper proposes a CTU/CU partitioning algorithm based on a pre-analysis stage. This stage performs a fast motion estimation that provides preliminary information to the encoder, including estimate distortion costs, which enables the building of the least-cost quadtree. On the basis of this tree, the evaluation performed by the encoder is restricted to a subset of PUs. After a thorough statistical analysis of numerous CU/PU combinations, we propose two different configurations that prioritize each of the target variables: coding efficiency and time reduction. In the former case, 58.09% of the encoding time can be saved at the cost of a 2.51% increase in Bjontegaard delta (BD)-rate, while a larger reduction of 63.08% is achieved with a 3.43% increase in BD-rate in the later case.

Adaptive CU Mode Selection in HEVC Intra Prediction: A Deep Learning Approach

The computational time of HEVC encoder is increased mainly because of the hierarchical quad-tree-based structure, recursive coding units, and the exhaustive prediction search up to 35 modes. These advances improve the coding efficiency, but result in a very high computational complexity. Furthermore, selecting the optimal modes among all prediction modes is necessary for subsequent rate-distortion optimization process. Therefore, we propose a convolution neural network-based algorithm which learns the region-wise image features and performs a classification job. These classification results are later used in the encoder downstream systems for finding the optimal coding units in each of the tree blocks, and subsequently reduce the number of prediction modes. The experimental results show that our proposed learning-based algorithm reduces the encoder time saving up to 66.89% with a minimal Bjontegaard delta bit rate (BD-BR) loss of 1.31% over the state-of-the-art machine learning approaches. Furthermore, our method also reduces the mode selection by 45.83% with respect to the HEVC baseline.

A fast CU partition method based on CU depth spatial correlation and RD cost characteristics for HEVC intra coding

The High Efficiency Video Coding (HEVC) could save half of bitrates than the previous standard AVC/H.264. However, the traverse of quad-tree partition for finding the optimal CU partition structure brings high computational complexity, which greatly restricts its development in real-time video application. In this paper, a fast Coding Unit (CU) partition algorithm is proposed, which exploits early-split and early-termination strategies for CU partition based on spatial depth correlation and Rate–Distortion (RD) Cost Characteristics. The CU’s candidate depth range can be estimated by utilizing spatial neighboring CUs’ correlation in partition depth. So if the current depth is not in the candidate range, Rough Mode Decision (RMD) and Rate–Distortion Optimization (RDO) could be directly skipped. Moreover, RD cost would be set to an adaptive threshold based on training frames. When a CU’s RD cost is smaller than the threshold, quad-tree partition would be early terminated. Numerous experimental results demonstrate that our algorithm can save up to 67.41% and 43.37% on average in encoding time for all intra sequences with 0.0276 decrease in PSNR and 0.6161% increase in encoding bitrates compared with the reference test model (HM).

Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

The Multi-View extension of High Efficiency Video Coding (MV-HEVC) has improved the coding efficiency of multi-view videos, but this comes at the cost of the extra coding complexity of the MV-HEVC encoder. This coding complexity can be reduced by efficiently reducing time-consuming encoding operations. In this work, we propose two methods to reduce the encoder complexity. The first one is Early Coding unit Splitting (ECS), and the second is the Efficient Reference Picture Selection (ERPS) method. In the ECS method, the decision of Coding Unit (CU) splitting for dependent views is made on the CU splitting information obtained from the base view, while the ERPS method for dependent views is based on selecting reference pictures on the basis of the temporal location of the picture being encoded. Simulation results reveal that our proposed methods approximately reduce the encoding time by 58% when compared with HTM (16.2), the reference encoder for MV-HEVC.

Quality Enhancement Network via Multi-Reconstruction Recursive Residual Learning for Video Coding

Lossy compression algorithms introduce multiple compression artifacts that severely decrease visual quality. These compression artifacts are highly related to texture contents, and the hierarchical coding units decision structure also brings multi-scale similarity to these artifacts. Current loop filters fail to utilize these characteristics to comprehensively remove compression artifacts. To this end, this letter proposes a novel quality enhancement method by adopting a multi-reconstruction recurrent residual network (MRRN). In particular, a modified recursive residual structure is designed to capture the multi-scale similarity of compression artifact. To effectively enhance frames with uneven noise, a multi-reconstruction structure is proposed, which outputs images with different denoise ratios and adaptively fuses them. Experimental results show that the proposed MRRN can improve coding efficiency up to 15.1% compared with the original loop filter in high-efficiency video coding. Averagely, 6.7%, 7.8%, 7.6% BD-rate reduction is achieved for all intra, low-delay P, and low-delay B, respectively. Meanwhile, as a quality enhancement method performed at encoder side, MRRN also achieves a good balance between coding performance and computational complexity compared to the state-of-the-art methods.

Bases are Not Letters: On the Analogy between the Genetic Code and Natural Language by Sequence Analysis

The article deals with the notion of the genetic code and its metaphorical understanding as a “language”. In the traditional view of the language metaphor of the genetic code, combinations of nucleotides are signs of amino acids (see the table of the genetic code). Similarly, words combined from letters (speech sounds) represent certain meanings. The language metaphor of the genetic code (Markos and Faltýnek, Biosemiotics 4(2), 171–200, 2011) assumes that the nucleotides stay in the analogy to letters, triples to words and genes to sentences (Jakobson 1971). We propose an application of mathematical linguistic methods on the notion of the genetic code. We provide quantitative analysis (n-gram structure, Zipf’s law) of mRNA strings and natural language texts. This analysis is sensitive to the detection of the code (language) units hierarchy. We also take into consideration a representative quantitative analysis of DNA, RNA and proteins. Our analysis of mRNA confirms an assumption that the design of the genetic code cannot analogize DNA bases and letters. The notion of the letter is much more appropriate if analogized with triplets or amino acids (see Lackova et al, Theory in Biosciences 136(3–4), 187–191, 2017).

Machine Learning-Based Fast Intra Mode Decision for HEVC Screen Content Coding via Decision Trees

The screen content coding (SCC) extension of high efficiency video coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, namely, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree-based framework for fast intra mode decision by investigating various features in the training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches where both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either the IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced, which reveal the unique intermediate coding information of a coding unit (CU). Then, if all the modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU-type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate–distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjontegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configurations, which outperforms all the state-of-the-art algorithms in the literature.

Intra-Prediction Mode Propagation for Video Coding

Intra-prediction and inter-prediction are two major ways of prediction in video coding. In the state-of-the-art video coding schemes such as joint exploration model (JEM), these two kinds of prediction are performed independently as different modes. An inter-coded coding unit (CU) is only assigned with the motion information, without an intra-prediction mode (IPM). To capture the texture direction in an inter-coded CU with low complexity, we present an IPM propagation approach in this paper. In the proposed scheme, each $4\times 4$ sub-block in an inter-coded CU is assigned with a propagated IPM, which is fetched from a reference block, located by a motion vector. The propagated IPM is utilized in two aspects. First, the propagated IPM can help to perform inter–intra-prediction in the inter-coded CU. Inter-prediction controlled by the motion information and intra-prediction controlled by the propagated IPM are both performed. The final prediction is generated by a position-dependent weighted sum of the two kinds of prediction. Second, the propagated IPMs of neighboring inter-coded blocks can help to predict the IPM of the intra-coded CU in an inter-picture. The propagated IPM of an inter-coded neighboring block can also be treated as a most probable mode. Simulation results show that BD rate savings of 0.5%, 1.0%, and 1.0% on average for Y, Cb, and Cr components, respectively, are achieved in random access configurations under the common test condition, compared with JEM-7.1. On ultra-high definition sequences, the BD rate savings can reach 1.0%, 1.7%, and 2.3% for Y, Cb, and Cr components, respectively.

Fast 3D-HEVC Depth Map Encoding Using Machine Learning

This paper presents a fast depth map encoding for 3D-High Efficiency Video Coding (3D-HEVC) based on static decision trees. We used data mining and machine learning to correlate the encoder context attributes, building the static decision trees. Each decision tree defines that a depth map Coding Unit (CU) must be or not be split into smaller blocks, considering the encoding context through the evaluation of the encoder attributes. Specialized decision trees for I-frames, P-frames and B-frames define the partitioning of $64\times 64$ , $32\times 32$ , and $16\times 16$ CUs. We trained the decision trees using data extracted from the 3D-HEVC Test Model considering all-intra and random-access configurations, and we evaluated the proposed approach considering the common test conditions. The experimental results demonstrated that this approach can halve the 3D-HEVC encoder computational effort with less than 0.24% of BD-rate increase on the average for all-intra configuration. When running on random-access configuration, our solution is able to reduce up to 58% the complete 3D-HEVC encoder computational effort with a BD-rate drop of only 0.13%. These results surpass all related works regarding computational effort reduction and BD-rate.

Reinforcement learning based coding unit early termination algorithm for high efficiency video coding

In this paper, we propose a Reinforcement Learning (RL) based Coding Unit (CU) early termination algorithm for High Efficiency Video Coding (HEVC). RL is utilized to learn a CU early termination classifier independent of depths for low complexity video coding. Firstly, we model the process of CU decision as a Markov Decision Process (MDP) according to the Markov property of CU decision. Secondly, based on the MDP, a CU early termination classifier independent of depths is learned from trajectories of CU decision across different depths with the end-to-end actor-critic RL algorithm. Finally, a CU decision early termination algorithm is introduced with the learned classifier, so as to reduce computational complexity of CU decision. We implement the proposed scheme with different neural network structures. Two different neural network structures are utilized in the implementation of RL based video encoder, which are evaluated to reduce video coding complexity by 34.34% and 43.33%. With regard to Bjøntegaard delta peak signal-to-noise ratio and Bjøntegaard delta bit rate, the results are −0.033 dB and 0.85%, −0.099 dB and 2.56% respectively on average under low delay B main configuration, when compared with the HEVC test model version 16.5.

Anomalously Perfect Reflections Based on 3‐Bit Coding Metasurfaces

AbstractHerein, a new design method to realize anomalously perfect reflections based on digital coding metasurfaces is proposed. Previous studies on perfect reflections usually rely on impedance matching, which requires complicated analyses and calculations. Based on the digital design of coding metasurfaces, a simple design method to generate anomalously perfect reflections for different reflection angles with high efficiency is presented. 3‐bit digital coding units are designed and verified to provide reflection phase responses of 0°, 45°, 90°, 133°, 176°, 221°, 269°, and 302°. Four digital coding patterns for different reflection angles are created to demonstrate the anomalously perfect reflections. Numerical simulations and experimental results have good agreements, suggesting the powerful potentials of the digital coding metasurface in controlling the electromagnetic waves.

Reduced-Complexity Intra Block Copy (IntraBC) Mode With Early CU Splitting and Pruning for HEVC Screen Content Coding

A screen content coding (SCC) extension to high efficiency video coding has been developed to incorporate many new coding tools in order to achieve better coding efficiency for videos mixed with camera-captured content and graphics/text/animation. For instance, the Intra Block Copy (IntraBC) mode helps to encode repeating patterns within the same frame while the Palette mode aims at encoding screen content with a few major colors. However, the IntraBC mode brings along high computational complexity due to the exhaustive block matching within the same frame though there are already some constraints and fast approaches applied to the IntraBC mode to reduce its complexity. Thus, we propose a fast intracoding scheme to reduce the complexity of using the IntraBC mode in SCC. Screen content always contains no sensor noise resulting in the characteristics with pixel exactness along both horizontal and vertical directions. These characteristics pave the way for mode skipping and early coding unit (CU) splitting. Besides, early CU pruning and early termination are proposed based on the rate distortion cost to further reduce encoder complexity. Moreover, we also propose reducing the complexity of the IntraBC mode by checking the hash value of each block candidate and the current block during block matching. With our proposed scheme, the encoding time is reduced compared with the SCC while the coding efficiency can still be maintained with a minor increase in the bjontegaard delta bitrate.

Informational Needs of Patients with Cancer: A Qualitative Content Analysis

Section TitleOne of the most important needs of patients with cancer is informational needs. Getting relevant information about the disease can lead to a proper decision making, better response to treatment, lower levels of anxiety, and higher levels of quality of life. Therefore, the current study aimed to determine the informational needs of patients with cancer in southeast Iran. Materials and Methods:This conventional qualitative content analysis was done using a descriptive-explorative design. Data collection was done through conducting deep semi-structured interviews from September 2017 to March 2018 in cancer treatment centers of Kerman University of Medical Science, Iran. Data saturation was achieved after interviewing with 13 patients during 15 interviews. The patients were asked to narrate their experience about informational needs of cancer patients. The following concepts were considered: the unit of analysis, meaning unit, condensation, code, sub-category, category, and main category. Results:The results of the present study showed that informational needs of these patients could be categorized under one main category called awareness-oriented needs, including three subcategories. These subcategories included lack of knowledge about the nature of the disease, inappropriate control of the disease due to lack of knowledge, and lack of knowledge about cancer treatment methods. Conclusion:Results showed that patients with cancer tended to know what is the cancer and the ways of cancer treatment and complications. Therefore, it is suggested to perform further studies cultivating the awareness of cancer patients in these areas. Therefore So, It should be noted that designing and implementation of this needs assessment provide a comprehensive way to consult and collaborate with health care professionals, patients, and their families.

A ReRAM Macro Using Dynamic Trip-Point-Mismatch Sampling Current-Mode Sense Amplifier and Low-DC Voltage-Mode Write-Termination Scheme Against Resistance and Write-Delay Variation

Many cost-aware IoE devices require embedded nonvolatile memory (eNVM) to achieve high-speed read and low-power write operations for serving as code and data storage unit. Resistive random access memory (ReRAM) is a good candidate for eNVM of Internet-of-Everything (IoE) but suffers low read yield and require long read latency ( $T_{\text {CD}}$ ) against small $R$ -ratio, large cell-resistance variations, and device-mismatch induced input offset at current-mode sense amplifier (CSA). The wide distribution in write time also causes large wasted write power ( $E_{\text {W}}$ ) and long NVM-stress-time ( $T_{\text {STRS}}$ ). This paper proposes a bitline-current-aware small-offset CSA, using dynamic trip-point-mismatch sampling (DTPMS) scheme, to improve read yield and shorten $T_{\text {CD}}$ . This paper also proposes a low dc-current voltage-mode write termination (LDC-VWT) module, including SET and RESET termination circuits, to suppress $E_{\text {W}}$ and $T_{\text {STRS}}$ . A fabricated 65-nm 2-Mb ReRAM macro achieved $T_{\text {CD}}= 2.6$ ns and confirm the write-termination operations for reduction in $T_{\text {STRS}}$ and $E_{\text {W}}$ .

Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric.

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.