scholarly journals An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 380
Author(s):  
Yuqun Xue ◽  
Yongsen Wang ◽  
Jianhua Jiang ◽  
Zenghui Yu ◽  
Yi Zhan ◽  
...  
Keyword(s):  
Video Compression ◽  
Vector Quantization ◽  
High Performance ◽  
Search Algorithm ◽  
Code Word ◽  
Training Procedure ◽  
Compression Technique ◽  
Squared Error ◽  
Partial Distortion ◽  
Computation Load

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm.

scholarly journals Video compression based on Multiwavelet and Multi Stage Vector Quantization using Adaptive Diamond Refinement Search Algorithm

2019 ◽  
Vol 8 (2S5) ◽  
pp. 203-207
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
Vector Quantization ◽  
Processing Speed ◽  
Search Algorithm ◽  
Essential Requirement ◽  
Second Stage ◽  
Multi Stage ◽  
Third Stage ◽  
Good Compression

Due to the advances in the digital technology, multimedia processing has become the essential requirement in many applications. These applications find wide use in mobile, personal computer(PC), TV, surveillance and satellite broadcast. Also it is necessary that the video coding algorithms to be updated in order to meet the requirements of latest hardware devices. The processing speed and bandwidth are essential parameters in these applications. A good video compression standard can achieve these parameters adequately. In the proposed system, the video coding standard is implemented using the three important stages. In which the first sage uses multiwavelets to achieve good compression rate. Also it reduces the memory and bandwidth requirement. Second stage is the Multi Stage Vector Quantization(MVSQ) which reduces the complexity of searching process and the size of codebook. Third stage uses Adaptive Diamond Refinement Search(ADRS) algorithm for the motion estimation which has better performance than the Adaptive Diamond Orthogonal Search(ADOS) and Diamond Refinement Search(DRS) algorithms. The combination of multiwavelet, Multi Stage Vector Quantization(MVSQ) and Adaptive Diamond Refinement Search(ADRS) algorithm gives the high compression ratios. Preliminary results indicate that the proposed method has good performance in terms of average number of search points, PSNR values and compression rates.

Parallel Full Search Algorithm for Motion Estimation on Graphic Processing Unit

2019 ◽  
Vol 12 (4) ◽  
pp. 317-323
Author(s):  
Fatma Ezzahra Sayadi ◽  
Marwa Chouchene ◽  
Haithem Bahri ◽  
Randa Khemiri ◽  
Mohamed Atri
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
High Efficiency ◽  
Search Algorithm ◽  
Low Cost ◽  
Video Quality ◽  
Processing Unit ◽  
High Efficiency Video Coding ◽  
Nvidia Gpu

Background: Advances in video compression technology have been driven by everincreasing processing power available in software and hardware. Methods: The emerging High-Efficiency Video Coding (HEVC) standard aims to provide a doubling in coding efficiency with respect to the H.264/AVC high profile, delivering the same video quality at half the bit rate. Results: Thus, the results show high computational complexity. In both standards, the motion estimation block presents a significant challenge in clock latency since it consumes more than 40% of the total encoding time. For these reasons, we proposed an optimized implementation of this algorithm on a low-cost NVIDIA GPU developed with CUDA language. Conclusion: This optimized implementation can provide high-performance video encoder where the speed reaches about 85.

scholarly journals Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Sima Ahmadpour ◽  
Tat-Chee Wan ◽  
Zohreh Toghrayee ◽  
Fariba HematiGazafi
Keyword(s):  
Video Compression ◽  
High Performance ◽  
Hypothesis Test ◽  
Video Data ◽  
Video Codec ◽  
Video Frame ◽  
Scalable Video ◽  
Compression Technique ◽  
Data Set ◽  
Frame Size

Designing an effective and high performance network requires an accurate characterization and modeling of network traffic. The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test. The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three different movies.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

Recurrent Neural Networks for Edge Intelligence

2021 ◽  
Vol 54 (4) ◽  
pp. 1-38
Author(s):  
Varsha S. Lalapura ◽  
J. Amudha ◽  
Hariramn Selvamuruga Satheesh
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Performance ◽  
Network Architectures ◽  
Training Procedure ◽  
Resource Constrained ◽  
Compression Technique ◽  
Creative Art ◽  
Comprehensive Survey ◽  
Intelligent Models

Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge “training havoc.” Current expansion of IoT demands intelligent models to be deployed at the edge. This is precisely to handle increasing model sizes and complex network architectures. Design efforts to meet these for greater performance have had inverse effects on portability on edge devices with real-time constraints of memory, latency, and energy. This article provides a detailed insight into various compression techniques widely disseminated in the deep learning regime. They have become key in mapping powerful RNNs onto resource-constrained devices. While compression of RNNs is the main focus of the survey, it also highlights challenges encountered while training. The training procedure directly influences model performance and compression alongside. Recent advancements to overcome the training challenges with their strengths and drawbacks are discussed. In short, the survey covers the three-step process, namely, architecture selection, efficient training process, and suitable compression technique applicable to a resource-constrained environment. It is thus one of the comprehensive survey guides a developer can adapt for a time-series problem context and an RNN solution for the edge.

scholarly journals A Novel Prediction-Based Asymmetric Fast Search Algorithm for Video Compression

10.5772/14684 ◽  
2011 ◽  
Author(s):  
Chung-Ming Kuo ◽  
Nai-Chung Yang ◽  
I-Chang Jou ◽  
Chaur-Heh Hsieh
Keyword(s):  
Video Compression ◽  
Search Algorithm ◽  
Fast Search ◽  
Fast Search Algorithm

An Improved Object-Based Video Segmentation Method

2012 ◽  
Vol 220-223 ◽  
pp. 2445-2449
Author(s):  
Wen Dan Xu ◽  
Xin Quan Lai ◽  
Dong Lai Xu
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
Video Segmentation ◽  
Error Function ◽  
Least Square ◽  
Video Frame ◽  
Merging Process ◽  
Initial Segmentation ◽  
Two Stages

This paper presents an improved video segmentation scheme, which consists of two stages: initial segmentation and motion estimation. In the initial segmentation, the watershed transformation followed by a region adjacency graph guided region merging process is used to partition the first video frame into spatial homogenous regions. Then the motion of changed region is estimated. Based on the highly efficient quadratic motion model, the motion estimation is undertaken using Gauss-Newton Levenberg-Marquardt method to minimize the least-square error function. Experimental results show the proposed scheme provides high performance in terms of segmentation accuracy and video compression ratio.

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