scholarly journals Simplification on Cross-Component Linear Model in Versatile Video Coding

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1885
Author(s):  
Sung-Chang Lim ◽  
Dae-Yeon Kim ◽  
Jungwon Kang
Keyword(s):  
Parallel Processing ◽  
Video Coding ◽  
Linear Model ◽  
Model Parameters ◽  
Test Model ◽  
Implementation Issue ◽  
Coding Efficiency ◽  
Current Block ◽  
Software Implementations ◽  
Chroma Components

To improve coding efficiency by exploiting the local inter-component redundancy between the luma and chroma components, the cross-component linear model (CCLM) is included in the versatile video coding (VVC) standard. In the CCLM mode, linear model parameters are derived from the neighboring luma and chroma samples of the current block. Furthermore, chroma samples are predicted by the reconstructed samples in the collocated luma block with the derived parameters. However, as the CCLM design in the VVC test model (VTM)-6.0 has many conditional branches in its processes to use only available neighboring samples, the CCLM implementation in parallel processing is limited. To address this implementation issue, this paper proposes including the neighboring sample generation as the first process of the CCLM, so as to simplify the succeeding CCLM processes. As unavailable neighboring samples are replaced with the adjacent available samples by the proposed CCLM, the neighboring sample availability checks can be removed. This results in simplified downsampling filter shapes for the luma sample. Therefore, the proposed CCLM can be efficiently implemented by employing parallel processing in both hardware and software implementations, owing to the removal of the neighboring sample availability checks and the simplification of the luma downsampling filters. The experimental results demonstrate that the proposed CCLM reduces the decoding runtime complexity of the CCLM mode, with negligible impact on the Bjøntegaard delta (BD)-rate.

The Improved AVS Intra Prediction Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 1730-1735
Author(s):  
Fu Jiang Li ◽  
Qing Chang
Keyword(s):  
Video Coding ◽  
Intra Prediction ◽  
Experimental Results ◽  
Prediction Algorithm ◽  
Bottom Block ◽  
Vertical Mode ◽  
Coding Efficiency ◽  
Prediction Mode ◽  
Current Block ◽  
The Right

AVS introduces intra-prediction coding technique to improve the intra-coding efficiency. There are 5 candidate modes for luminance intra-prediction and 4 candidate modes for chrominance. The method of intra prediction for the current video coding standard mainly uses the pixels of the left, the top, the upper right neighboring blocks, and doesnt use the pixels of the right and bottom blocks. It is not because there is low correlation between the current block and the right and bottom blocks, but the right and bottom blocks need to refer to the data of current block to decode the display. If the mode of the right block is the vertical mode, it can use the data of right block to predict the current block and a new prediction mode of mean-left-right is presented, and if the mode of the bottom block is the horizontal mode, it can use the data of bottom block to predict the current block and a new prediction mode of mean-top-bottom is presented. The corresponding adjustment is implemented for AVS encoder and decoder. Experimental results show that the PSNR increases about 0.03db and the coding bit rate decreases by about0.73% with small complexity increasing.

Complexity Optimization for the Upcoming Versatile Video Coding Standard

2020 ◽  
Vol 2020 (9) ◽  
pp. 286-1-286-7
Author(s):  
Sami JABALLAH ◽  
Mohamed-Chaker LARABI
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Block Size ◽  
Efficiency Improvement ◽  
Stopping Criterion ◽  
Coding Efficiency ◽  
Quad Tree ◽  
Prediction Mode ◽  
Current Block ◽  
Coding Unit

The Versatile Video Coding (VVC) is forseen as the next generation video coding standard. The main objective is to achieve coding efficiency improvement of about 50% bit-rate reduction compared to the previous standard HEVC at the same visual quality by 2020. In this paper, a fast VVC encoder is proposed based on an early split termination for fast intra CU selection. Taking into account edge complexity of the block and the best intra prediction mode obtained at the current block size, an early split termination is proposed. Using spatial neighboring coding unit depths (quad-tree, binary-tree and ternary-tree depths), the depth probability measure is computed and used to define the stopping criterion. The proposed algorithm is evaluated on nine commoly used test video sequences. Compared to the current VTM3.0 in all intra high efficiency and LowDelayP configuration cases, the proposed algorithm outperforms the anchor scheme in terms of encoding time with a slightly degradation in coding efficiency.

Decoder Derived Cross-Component Linear Model Intra-Prediction for Video Coding

2021 ◽  
Author(s):  
Zhipin Deng ◽  
Kai Zhang ◽  
Li Zhang
Keyword(s):  
Video Coding ◽  
Linear Model ◽  
Intra Prediction

scholarly journals Least Squares Estimations for the General Linear Model Parameters with Epsilon Skew Normal Error Term

2020 ◽  
pp. 636-645
Author(s):  
Hussain Karim Nashoor ◽  
Ebtisam Karim Abdulah
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Least Squares Method ◽  
General Linear Model ◽  
Ordinary Least Squares ◽  
Skewed Distribution ◽  
General Linear ◽  
Average Error ◽  
Model Parameters ◽  
Skew Normal

Examination of skewness makes academics more aware of the importance of accurate statistical analysis. Undoubtedly, most phenomena contain a certain percentage of skewness which resulted to the appearance of what is -called "asymmetry" and, consequently, the importance of the skew normal family . The epsilon skew normal distribution ESN (μ, σ, ε) is one of the probability distributions which provide a more flexible model because the skewness parameter provides the possibility to fluctuate from normal to skewed distribution. Theoretically, the estimation of linear regression model parameters, with an average error value that is not zero, is considered a major challenge due to having difficulties, as no explicit formula to calculate these estimates can be obtained. Practically, values for these estimates can be obtained only by referring to numerical methods. This research paper is dedicated to estimate parameters of the Epsilon Skew Normal General Linear Model (ESNGLM) using an adaptive least squares method, as along with the employment of the ordinary least squares method for estimating parameters of the General Linear Model (GLM). In addition, the coefficient of determination was used as a criterion to compare the models’ preference. These methods were applied to real data represented by dollar exchange rates. The Matlab software was applied in this work and the results showed that the ESNGLM represents a satisfactory model. 

scholarly journals A Tutorial on H.264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance

10.5772/19227 ◽  
2011 ◽  
Author(s):  
Iraide Unanue ◽  
Inigo Urteaga ◽  
Ronaldo Husemann ◽  
Javier Del ◽  
Valter Roesler ◽  
...  
Keyword(s):  
Video Coding ◽  
Scalable Video Coding ◽  
Scalable Video ◽  
Coding Efficiency ◽  
And Performance

scholarly journals Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

2017 ◽  
Vol 6 (3) ◽  
pp. 75
Author(s):  
Tiago V. F. Santana ◽  
Edwin M. M. Ortega ◽  
Gauss M. Cordeiro ◽  
Adriano K. Suzuki
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Likelihood Function ◽  
Confidence Bands ◽  
Model Parameters ◽  
Random Component ◽  
Monte Carlo Simulation Study ◽  
New Model ◽  
Exponentiated Weibull

A new regression model based on the exponentiated Weibull with the structure distribution and the structure of the generalized linear model, called the generalized exponentiated Weibull linear model (GEWLM), is proposed. The GEWLM is composed by three important structural parts: the random component, characterized by the distribution of the response variable; the systematic component, which includes the explanatory variables in the model by means of a linear structure; and a link function, which connects the systematic and random parts of the model. Explicit expressions for the logarithm of the likelihood function, score vector and observed and expected information matrices are presented. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. To detect influential observations in the new model, we use diagnostic measures based on the local influence and Bayesian case influence diagnostics. Also, we show that the estimates of the GEWLM are  robust to deal with the presence of outliers in the data. Additionally, to check whether the model supports its assumptions, to detect atypical observations and to verify the goodness-of-fit of the regression model, we define residuals based on the quantile function, and perform a Monte Carlo simulation study to construct confidence bands from the generated envelopes. We apply the new model to a dataset from the insurance area.

scholarly journals Generalized Born radii computation using linear models and neural networks

2019 ◽  
Vol 36 (6) ◽  
pp. 1757-1764
Author(s):  
Saida Saad Mohamed Mahmoud ◽  
Gennaro Esposito ◽  
Giuseppe Serra ◽  
Federico Fogolari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Model ◽  
Correlation Coefficient ◽  
Linear Models ◽  
Reference Method ◽  
Supplementary Information ◽  
Model Parameters ◽  
Generalized Born ◽  
Supplementary Material

Abstract Motivation Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. Results GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of ‘perfect’ GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with ‘perfect’ GB radii of 0.97 and ca. 20% smaller root mean square error. Availability and implementation We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

2020 ◽  
Vol 34 (07) ◽  
pp. 11580-11587
Author(s):  
Haojie Liu ◽  
Han Shen ◽  
Lichao Huang ◽  
Ming Lu ◽  
Tong Chen ◽  
...  
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Temporal Correlation ◽  
Second Order ◽  
Compression Method ◽  
High Efficiency Video Coding ◽  
First Order ◽  
Coding Efficiency ◽  
The Common

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.

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