Prediction Mode Research Articles

Double compression detection in HEVC-coded video with the same coding parameters using picture partitioning information

Detection of double compression, particularly in the high-efficiency video coding (HEVC) compressed domain, is one of the most operative and efficacious ways of authenticating the validity of videos in the field of forensic analysis. The strength of identifying abnormalities in the videos confides in diverse coding parameters (such as quantization parameters, size and structure of the group of pictures, and modes of compression). Many methods have been introduced to dig up HEVC double compression with different coding parameters. However, the revelation of the HEVC double compression under the same coding environments still remains a competitive task, as recompressions leave small footprints. In this paper, we introduce a novel method based on frame partitioning information to distinguish between single and double compressions with the same coding parameters. We propose extracting statistical and deep convolution neural network (DCNN) features from partition pictures and prediction modes, including coding unit, prediction unit, transform unit, and most probable modes information. Finally, machine learning technology is integrated to categorize videos into two classes, single and double compressions, by combining the statistical and DCNN features. We obtain the best experimental results by assembling the statistical and DCNN features for wide video graphics array (WVGA) and high-definition (HD) sequences with average accuracies of 99.66% and 99.60% in all-intra and 99.46% and 99.33% in low-delay P modes respectively. Experimental results of the proposed system show the effectiveness and efficiency over the state-of-the-art techniques in video forensic.

Effective Prediction Modes Design for Adaptive Compression With Application in Video Coding.

Adaptive prediction is an important tool for efficient compression of non-stationary signals. A common approach to achieve adaptivity is to switch between a set of prediction modes, designed to capture variations in signal statistics. The design poses several challenges including: i) catastrophic instability due to statistical mismatch driven by propagation through the prediction loop, and ii) severe non-convexity of the cost surface that is often riddled with poor local minima. Motivated by these challenges, this paper presents a near-optimal method for designing prediction modes for adaptive compression. The proposed method builds on a stable, open-loop platform, but with a subterfuge that ensures that the design is asymptotically optimized for closed-loop operation. The non-convexity is handled by deterministic annealing, a powerful optimization tool to avoid poor local minima. To demonstrate the impact of the proposed approach on practical applications, we consider adaptive, transform-domain predictor design for enhancing standard video coding. Experimental results validate the benefits of the proposed design in terms of significant performance gains for both predictive compression systems in general and video coding in particular.

Research on IMDB Film Score Prediction Based on Improved Whale Algorithm

In order to improve the prediction precision of film score, the improved whale algorithm is established to predict the IMDB film score. Firstly, the IMDB scoring mechanism is studied, the reason of inaccurate scoring or network "water army" is analyzed, and the score of the meta score, the number of reviews and the popularity index are discussed. Data of the film in system is updated in real time. Secondly, the improved whale optimization algorithm is constructed, the mathematical model of encircling prey, bubble net attacking and search for prey are discussed. The adaptive weight formula is established and the improved adaptive updating formula is constructed, and the corresponding algorithm procedure is designed. Finally, through the analysis of visual results, the key factors affecting film scoring are found out, and suggestions are given for the film recommendation system. One hundred films are selected to carry out prediction analysis, the top 85% films are used as training samples, and the bottom 15% films are used as testing samples. The prediction mode is trained through training samples, and the testing samples are predicted based on trained prediction model. A prediction simulation analysis is carried out using IMDB samples, and results show that the proposed prediction model has high prediction precision.

The Impact of Joint Versus Separate Prediction Mode on Forecasting Accuracy

The Impact of Joint Versus Separate Prediction Mode on Forecasting Accuracy

The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy

The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy

Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

High Efficiency Video Coding (HEVC) double compression detection is of prime significance in video forensics. However, double compression with the same parameters and video content with high motion displacement intensity have become two main factors that limit the performance of existing algorithms. To address these issues, a novel motion-adaptive algorithm is proposed in this paper. Firstly, the analysis of GOP structure in HEVC standard and the coding process of HEVC double compression are provided. Next, sub-features composed of fluctuation intensities of intra prediction modes and unstable Prediction Units (PUs) in normal Intra-Frames (I-frames) and optical flow in adaptive I-frames are exploited in our algorithm. Each sub-feature is extracted during the process of multiple decompression. We further combine these sub-features into a 27-dimensional detection feature, which is finally fed to the Support Vector Machine (SVM) classifier. By following a separation-fusion detection strategy, the experimental result shows that the proposed algorithm outperforms the existing state-of-the-art methods and demonstrates superior robustness to various motion displacement intensities and a wide variety of coding parameter settings.

High Efficiency Intra CU Partition and Mode Decision Method for VVC

Versatile video coding (VVC/H.266) is the newest video compression standard, which is developed by the Joint Video Experts Team. Compared with previous encoding schemes, VVC achieves higher compression efficiency by introducing a new partition structure and additional intra prediction modes but results in high computational complexity. To efficiently solve this problem of redundant processing in quad-tree with nested multi-type tree structures and intra mode prediction, we propose a texture analysis–based ternary tree (TT) and binary tree (BT) partition strategy, and a gradient-based intra mode decision method to accelerate TT and BT partition and intra mode prediction, separately. The texture complexity and prediction direction of coding unit (CU) is calculated by texture detection method. A texture analysis–based TT and BT partition strategy is established by using the regression method based on analyzing the texture complexity of the CU. Then, a texture analysis–based TT and BT partition strategy is applied to reduce the redundant partition for each CU. By using the prediction direction of CU, a gradient-based intra mode decision method is established for skipping the impossible modes for each CU. Experimental results revealed that the proposed method could save 49.49% in encoding time and increase the Bjontegaard delta bit rate (BDBR) by only 0.56%. It confirms that the proposed method achieved high efficiency and a good balance between the BDBR and time saving.

The Impact of Joint Versus Separate Prediction Mode on Forecasting Accuracy

The Impact of Joint Versus Separate Prediction Mode on Forecasting Accuracy

Fast Intra Mode Decision Algorithm for Versatile Video Coding

To achieve higher coding efficiency, the latest Versatile Video Coding (VVC) standard adopts a series of new intra coding techniques, including the quadtree plus multi-type tree (QTMT), intra sub-partitions (ISP) and intra block copy (IBC). However, this makes the intra coding more complicated, as VVC needs to traverse all prediction modes and partition types of QTMT to find the optimal combination. In this paper, we propose a fast algorithm for VVC from two aspects of mode selection and prediction terminating to reduce coding complexity. For the mode selection, adaptive mode pruning (AMP) is proposed to remove non-promising modes. First, since the newly introduced modes (IBC and ISP) are not effective for all blocks, learning-based classifiers are designed to remove them intelligently. Second, for normal modes, an ensemble decision strategy is proposed to sort the candidate modes and increase the probability of being the optimal mode for the first few candidates; thus, we can remove redundant candidates more efficiently. In terms of prediction terminating, we find that different optimal modes of current depth level lead to different termination probabilities of remaining intra predictions. Therefore, mode-dependent termination (MDT) is proposed to select an appropriate model through the optimal mode and terminate unnecessary intra predictions of remaining depth levels. The proposed algorithm is implemented on VVC test model, and simulation results show that it can achieve 51% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 53% time savings with only 0.93% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1.08% BDBR increases.

Improved tensile and single-lap-shear mechanical-electrical response of epoxy composites reinforced with gridded nano-carbons

A new carbon nanotube (CNT)/graphene (GN) grid structure is prepared and used to reinforce and health-monitor the epoxy composites. Results reveal that with a 30–50% mesoscopic-porosity of the CNT/GN grid, tensile strength and single-lap-shear (SLS) strength of the CNT/GN/epoxy composites can be improved by 27–41% and 28–49%, respectively. Also, relative resistance change (ΔR/R0%) at damage initiation would sudden change more than 10% for the tensile testing and 6% for the SLS testing. Meanwhile, during the damage evolution stage, the gauge factor obtained from tensile testing reaches 10.2–24.9 improved by 23–200%, and ΔR/R0% from SLS testing achieves 47.4–60.4% increased by 69–116%, respectively, when compared to the baseline composite. Moreover, an equivalent resistance prediction mode for the CNT/GN grid composites is proposed and which can well explain the resistance response mechanism. The result reveals that the gridded CNT/GN sensor is potential in reinforcing and structural health monitoring the epoxy resin-based composites.

Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics

To establish an ultrasound-based radiomics model through machine learning methods and then to assess the ability of the model to differentiate infected focal liver lesions from malignant mimickers. A total of 104 patients with infected focal liver lesions and 485 patients with malignant hepatic tumours were included, consisting of hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), combined hepatocellular-cholangiocarcinoma (cHCC-CC), and liver metastasis. Radiomics features were extracted from grey-scale ultrasound images. Feature selection and predictive modelling were carried out by dimensionality reduction methods and classifiers. The diagnostic effect of the prediction mode was assessed by receiver operating characteristic (ROC) curve analysis. In total, 5,234 radiomics features were extracted from grey-scale ultrasound image of every focal liver lesion. The ultrasound-based radiomics model had a favourable predictive value for differentiating infected focal liver lesions from malignant hepatic tumours, with an area under the curve (AUC) of 0.887 and 0.836 (HCC group), 0.896 and 0.766 (CC group), 0.944 and 0.754 (cHCC-CC group), 0.918 and 0.808 (liver metastasis group), and 0.949 and 0.745 (malignant hepatic tumour group) for the training set and validation set, respectively. Ultrasound-based radiomics is helpful in differentiating infected focal liver lesions from malignant mimickers and has the potential for use as a supplement to conventional grey-scale ultrasound and contrast-enhanced ultrasound (CEUS).

HEVC Information Hiding Algorithm Based on Intra Prediction and Matrix Coding

Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.

Applying Precision Agriculture to Artificial Waterfowl Hatching, Using the Black Muscovy Duck as an Example

(1) Background: agriculture practices adopt homogenization-farming processes to enhance product characteristics, with lower costs, standardization, mass production, and production efficiency. (2) Problem: conventional agriculture practices eliminate products when these products are slightly different from the expected status in each phase of the lifecycle due to the changing natural environment and climate. However, this elimination of products can be avoided when they receive customized care to the expected developing path via a universal prediction model, for the quantitative description of biomass changing with time and the environment, and the corresponding automatic environmental controls. (3) Methods: in this study, we built a prediction model to quantitatively predict the hatching rate of each egg by observing the biomass development path along the waterfowl-like production lifecycle and the corresponding environment settings. (4) Results: two experiments using black Muscovy duck hatching as a case study were executed. The first experiment involved finding out the key characteristics, out of 25 characteristics, and building a prediction model to quantitatively predict the survivability of the black Muscovy duck egg. The second experiment was adopted to validate the effectiveness of our prediction mode; the hatching rate rose from 47% in the first experiment to 62% in the second experiment without any human interference from experienced farmers. (5) Contributions: this research builds on an AI-based precision agriculture system prototype as the reference for waterfowl research. The results show that our proposed model is capable of decreasing the training costs and enhancing the product qualification rate for individual agricultural products.

The Assessment of Intrapartum Transperineal Ultrasonographic Parameters for their Effectiveness in Evaluation of Progress of Labor and Prediction of Mode of Delivery in Egyptian Women

Introduction: High fetal head station has been associated with prolonged labor and delivery outcomes. Although clinical assessment of fetal head station is both subjective and unreliable, women with prolonged labor are subjected to multiple digital vaginal examinations. The use of ultrasound has been proposed to aid in the management of labor since 1990s. Ultrasound examination is more accurate and reproducible than clinical examination in the diagnosis of fetal head station and in the prediction of arrest of labor. Ultrasound examination can, to some extent, distinguish those women destined for spontaneous vaginal delivery and those destined for operative delivery and may predict the outcome of instrumental vaginal delivery. Such a technique has the potential to reduce the frequency of intrusive internal examinations and associated infection and could be useful in allowing the assessment of women in whom digital VE is traumatic or contra-indicated. Intrapartum ultrasound not only provides objective and quantitative data in labor, but also helps to make more reliable clinical decisions aiming to improve obstetric outcomes of both the mother and fetus as a supplementary tool for active management.&#x0D; Aim of the work: This study aims at assessing the value of intrapartum transperineal ultrasonography as a quantitative and objective tool in the evaluation of progress of labor and prediction of mode of delivery.&#x0D; Subjects: This study was a prospective observational study conducted on 600 primiparous women in active first stage of labor admitted to Kasr Al Ainy maternity hospital from January 2017 to June 2018. The studied population was divided into two groups. Group A of 300 women with normal progress of labor and group B of 300 women with prolonged 1st stage of labor.&#x0D; Methods: Fetal head station(FHS) was assessed clinically by digital vaginal examination (dVE) and sonographically by transperineal ultrasound measurement of head perineal distance (HPD) and angle of progression (AOP). Intrapartum care of the patient continued as normal based only on digital vaginal examinations using the modified WHO partogram. (1). Statistical analysis was targeted towards assessing the potential of the intrapartum ultrasonography in the evaluation of progress of labor and prediction of mode of delivery.&#x0D; Results: All studied parameters for assessment of FHS (dVE, HPD, and AOP) significantly corelated with each other and with both progress of labor and mode of delivery with P value (&lt;0.001). The highest sensitivity for prediction of progress of labor is observed using dVE (83%), the highest specificity is observed using AOP (78.3%). The highest sensitivity for prediction mode of delivery is for combined HPD &amp; AOP (97.7%) while the highest specificity is for AOP (81%). When combining both HPD and AOP for prediction of mode of delivery, the assessment of both parameters was found to have a high sensitivity of 97.7% and a high positive predictive value of 86.63%.&#x0D; Conclusion: Intrapartum ultrasound examination is a valuable tool in the prediction of progress of labor and mode of delivery. The assessment of fetal head station by transperineal ultrasound measurement of HPD and AOP is much more informative of the progress of labor and the mode of delivery than digital assessment of fetal head station.&#x0D; Keywords: Labor, intrapartum ultrasound, Angle of progression, Head perineal distance, fetal head station, digital vaginal examination.

Motion Vector Coding and Block Merging in the Versatile Video Coding Standard

This paper overviews the motion vector coding and block merging techniques in the Versatile Video Coding (VVC) standard developed by the Joint Video Experts Team (JVET). In general, inter-prediction techniques in VVC can be classified into two major groups: “whole block-based inter prediction” and “subblock-based inter prediction”. In this paper, we focus on techniques for whole block-based inter prediction. As in its predecessor, High Efficiency Video Coding (HEVC), whole block-based inter prediction in VVC is represented by adaptive motion vector prediction (AMVP) mode or merge mode. Newly introduced features purely for AMVP mode include symmetric motion vector difference and adaptive motion vector resolution. The features purely for merge mode include pairwise average merge, merge with motion vector difference, combined inter-intra prediction and geometric partitioning mode. Coding tools such as history-based motion vector prediction and bidirectional prediction with coding unit weights can be applied on both AMVP mode and merge mode. This paper discusses the design principles and the implementation of the new inter-prediction methods. Using objective metrics, simulation results show that the methods overviewed in the paper can jointly achieve 6.2% and 4.7% BD-rate savings on average with the random access and low-delay configurations, respectively. Significant subjective picture quality improvements of some tools are also reported when comparing the resulting pictures at same bitrates.

Intra Prediction and Mode Coding in VVC

This paper presents the intra prediction and mode coding of the Versatile Video Coding (VVC) standard. This standard was collaboratively developed by the Joint Video Experts Team (JVET). It follows the traditional architecture of a hybrid block-based codec that was also the basis of previous standards. Almost all intra prediction features of VVC either contain substantial modifications in comparison with its predecessor H.265/HEVC or were newly added. The key aspects of these tools are the following: 65 angular intra prediction modes with block shape-adaptive directions and 4-tap interpolation filters are supported as well as the DC and Planar mode, Position Dependent Prediction Combination is applied for most of these modes, Multiple Reference Line Prediction can be used, an intra block can be further subdivided by the Intra Subpartition mode, Matrix-based Intra Prediction is supported, and the chroma prediction signal can be generated by the Cross Component Linear Model method. Finally, the intra prediction mode in VVC is coded separately for luma and chroma. Here, a Most Probable Mode list containing six modes is applied for luma. The individual compression performance of tools is reported in this paper. For the full VVC intra codec, a bitrate saving of 25% on average is reported over H.265/HEVC using an objective metric. Significant subjective benefits are illustrated with specific examples.

CNN-based Fast Split Mode Decision Algorithm for Versatile Video Coding (VVC) Inter Prediction

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites

Despite the existence of several stochastic streamflow generators, not much attention has been given to representing the impacts of large-scale climate indices on seasonal to interannual streamflow variability. By merging a formal predictor selection scheme with vine copulas, we propose a generic approach to explicitly incorporate large-scale climate indices in ensemble streamflow generation at single and multiple sites and in both short-term prediction and long-term projection modes. The proposed framework is applied at three headwater streams in the Oldman River Basin in southern Alberta, Canada. The results demonstrate higher skills than existing models both in terms of representing intra- and inter-annual variability, as well as accuracy and predictability of streamflow, particularly during high flow seasons. The proposed algorithm presents a globally relevant scheme for the stochastic streamflow generation, where the impacts of large-scale climate indices on streamflow variability across time and space are significant.

HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding

Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.

Joint Lossless Image Compression and Encryption Scheme Based on CALIC and Hyperchaotic System.

For efficiency and security of image transmission and storage, the joint image compression and encryption method that performs compression and encryption in a single step is a promising solution due to better security. Moreover, on some important occasions, it is necessary to save images in high quality by lossless compression. Thus, a joint lossless image compression and encryption scheme based on a context-based adaptive lossless image codec (CALIC) and hyperchaotic system is proposed to achieve lossless image encryption and compression simultaneously. Making use of the characteristics of CALIC, four encryption locations are designed to realize joint image compression and encryption: encryption for the predicted values of pixels based on gradient-adjusted prediction (GAP), encryption for the final prediction error, encryption for two lines of pixel values needed by prediction mode and encryption for the entropy coding file. Moreover, a new four-dimensional hyperchaotic system and plaintext-related encryption based on table lookup are all used to enhance the security. The security tests show information entropy, correlation and key sensitivity of the proposed methods reach 7.997, 0.01 and 0.4998, respectively. This indicates that the proposed methods have good security. Meanwhile, compared to original CALIC without security, the proposed methods increase the security and reduce the compression ratio by only 6.3%. The test results indicate that the proposed methods have high security and good lossless compression performance.