scholarly journals State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

2021 ◽  
Vol 12 (5) ◽  
pp. 1256-1272
Author(s):  
G. Megala, Et. al.
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
Video Sequence ◽  
Vital Role ◽  
Block Matching ◽  
Video Data ◽  
Perceptual Quality ◽  
Main Task ◽  
Compressed Video ◽  
Multimedia Services

Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

scholarly journals The data conundrum: compression of automotive imaging data and deep neural network based perception

2021 ◽  
Vol 2021 (1) ◽  
pp. 78-82
Author(s):  
Pak Hung Chan ◽  
Georgina Souvalioti ◽  
Anthony Huggett ◽  
Graham Kirsch ◽  
Valentina Donzella
Keyword(s):  
Neural Network ◽  
Video Compression ◽  
Situational Awareness ◽  
Video Camera ◽  
Video Data ◽  
Compressed Video ◽  
Imaging Data ◽  
The Neural Network ◽  
Perception System ◽  
Driving Assistance

Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.

The Efficiency of Data Hiding by Manipulating the DPCM Macroblocks in Compressed Video

2011 ◽  
Vol 230-232 ◽  
pp. 69-74
Author(s):  
Tarik Idbeaa ◽  
Kasmiran Jumari ◽  
Salina Abd. Samad ◽  
Ali Abdulgader ◽  
Nidal Eshah
Keyword(s):  
Video Compression ◽  
Data Hiding ◽  
Significant Loss ◽  
Low Noise ◽  
Video Data ◽  
Human Vision ◽  
Compressed Video ◽  
Secret Data ◽  
Still Images ◽  
High Security

Steganography is the idea of embedding a secret data in different media and has become an important regulation of methods of data integration. Although the still images are generally applied in the past, is very popular in recent years for the video. The techniques of video data hiding in recent year’s emphasis on the features generated by the video compression standard, a safer method for steganography uses MPEG-4/H.264 Bit Plane Complexity Segmentation (BPCS) algorithm is proposed in this approach. The reason for choosing such a video coverage is the enormous amount of data that can be hidden in each frame of MPEG-4 video. In other words, MPEG-4 has three types of images: I-frame, B, and P frames. Unlike other techniques used to hide data, such as the LSB algorithm, PBCS can achieve better results in both mathematics expression and human vision. In this paper, data is embedded in the videos of the I-frame until the BPCS can reach high levels of integration with low distortion based on the theory that regions of low noise-levels as in a picture can be replaced by noise without a significant loss of image quality. This approach invents data hidden in high-security environments. Experimental results show the success of hidden data in the selected and extracted data from the sequence of frames and also indicate the effectiveness of the implementation plan of steganography compressed video with high security features.

scholarly journals SIMULATION BASED VIDEO COMPRESSION THROUGH DIGITAL COMMUNICATION SYSTEM

2017 ◽  
Vol 5 (4RACEEE) ◽  
pp. 85-91
Author(s):  
Pundaraja ◽  
Manjunath
Keyword(s):  
Video Compression ◽  
Communication System ◽  
Video Transmission ◽  
Digital Communication ◽  
Video Data ◽  
Huffman Coding ◽  
Compressed Video ◽  
Channel Codes ◽  
Digital Communication System ◽  
Small Change

The paper is about the transmission, compression, detection of the video based on simulation for the various communication applications. The video and image compression overcomes the problem of reducing the amount of data required to the information that has to be transmitted and this saves the bandwidth required for transmission of data and memory which is required for storage purpose. Hence video compression reduces the volume of the video data with a small change in quality of the video. Compressed video transmission can be done over a channel by huffman coding for the source at transmitter side and then channel codes is done by technique called hamming. The data which is to be sent through channel is a BPSK modulated so the received data is demodulated followed by the channel decoding, source decoding using inverse of the techniques used in the transmitter side to obtain the original transmitted video. The above procedure is done for the input video taken by camera and this compressed video can be transmitted then detected at receiver by digital communication system(DCS) which is simulated in the MATLAB.

scholarly journals Compressed Video Quality

2018 ◽  
Author(s):  
Iain Richardson
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
Digital Video ◽  
Video Quality ◽  
Quality Measurement ◽  
Visual Quality ◽  
Compressed Video ◽  
High Definition ◽  
And Performance ◽  
Analogue To Digital

The concept of video compression goes hand in hand with the switch from analogue to digital video technology that has taken place over the last 25 years. Video delivered to televisions, computers and smartphones typically arrives in a compressed form. The bandwidth and file size savings that compression provides are a significant benefit for consumer and business applications, making it possible to send and receive high-definition video over limited capacity networks. However, for digital archive applications, compression can be problematic, especially when it introduces loss or distortion into a video signal.  ‘Born digital’ often means ‘born compressed’ and it is increasingly likely that newly-created digital video material will have gone through at least some level of lossy compression. For this reason, it is important to understand the effect of video compression on visual quality. In this paper I will introduce the concept of video compression and its relationship to video image quality. I will consider the factors that influence visual quality, including technical factors such as codecs and coding parameters, as well as the complex and only partly-understood factors that govern our perception of moving images. I will introduce methods of measuring and quantifying video quality and show how it is possible to compare the quality and performance of video processing systems, despite the limitations of quality measurement.

scholarly journals The video processing features research in computer systems and special purpose networks

2020 ◽  
pp. 126-132
Author(s):  
Ю.І. Шевяков ◽  
В.В. Ларін ◽  
є.л. Казаков ◽  
Ахмед Абдалла
Keyword(s):  
Correlation Coefficient ◽  
Compression Ratio ◽  
Video Processing ◽  
Video Sequence ◽  
Block Code ◽  
Low Complexity ◽  
Video Data ◽  
Compact Representation ◽  
Binary Mask ◽  
Subsequent Formation

For a typical low complexity video sequence, the weight of each P-frame in the stream is approximately three times smaller than the I-frame weight. However, taking into account the number of P-frames in the group, they make the main contribution to the total video data amount. Therefore, the possibility of upgrading coding methods for P-frames is considered on preliminary blocks' type identification with the subsequent formation of block code structures. As the correlation coefficient between adjacent frames increases, the compression ratio of the differential-represented frame's binary mask increases. The compression ratio of the differential-represented frame's binary mask varies from 3 to 21 depending on the correlation coefficient between adjacent frames. The most preferable method for constructing the compact representation technology of the binary masks of frames represented in a differential form is the approach. This is based on the identification and description of the lengths of one-dimensional binary series. A binary series is a consecutive binary elements sequence with the same value. In this case, sequences of identical binary elements are replaced by their lengths.

scholarly journals A New Block Matching Algorithm for Motion Estimation

2016 ◽  
Vol 855 ◽  
pp. 178-183 ◽  
Author(s):  
Chia Ming Wu ◽  
Jen Yi Huang
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Motion Compensation ◽  
Video Processing ◽  
Motion Vector ◽  
Block Matching ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Fast Motion ◽  
Better Than

Motion estimation has been the most key role on video processing. It is usually applied to block matching algorithm for choosing the best motion vector. The two adjacent images are searched to find the displacement of the same object in the video image. Many fast motion vector block matching algorithms are proposed, and they achieve the efficiency of motion compensation and video compression. In our paper, we propose a new algorithm that is based on ARPS. The experimental results show that the PSNR of the proposed method is better than that of other block matching methods on many kinds of video.

scholarly journals Bandwidth Estimation and Optimized Bitrate Selection for Dynamic Adaptive Streaming Over Http Using RSI-GM And ISSO

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Video Compression ◽  
Geometric Mean ◽  
Relative Strength ◽  
Video Data ◽  
Adaptive Streaming ◽  
Compressed Video ◽  
Available Bandwidth ◽  
Media Presentation ◽  
High Bandwidth ◽  
The Media

Dynamic Adaptive Streaming over HTTP (DASH) is an emerging solution that aims to standardize existing proprietary streaming systems. DASH specification defines the media presentation description (MPD), which describes a list of available content, URL addresses, and the segment format. High bandwidth demands in interactive streaming applications pose challenges in efficiently utilizing the available bandwidth. In this paper, a novel Relative Strength Index (RSI) with Geometric mean (GM) namely RSI-GM is proposed for estimating available bandwidth for DASH. The proposed work starts by taking the video as an input at the transmitter side and then the video compression is performed using the TRLE. Then MD5 hashing-based AES encryption is applied to the compressed video data to provide data security. Then RSI-GM is proposed to estimate the available bandwidth for DASH. Finally, after estimation, the bitrate for estimated bandwidth is selected optimally using the Improved Shark Smell Optimization (ISSO) algorithm.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.

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