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.

Applied Research of Assessment Methods on Video Quality

2012 ◽  
Vol 262 ◽  
pp. 157-162
Author(s):  
Chong Gu ◽  
Zhan Jun Si
Keyword(s):  
Video Processing ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Rapid Development ◽  
Video Quality ◽  
Objective Evaluation ◽  
Video Data ◽  
Assessment System ◽  
Video Encoding ◽  
High Definition

With the rapid development of modern video technology, the range of video applications is increasing, such as online video conferencing, online classroom, online medical, etc. However, due to the quantity of video data is large, video has to be compressed and encoded appropriately, but the encoding process may cause some distortions on video quality. Therefore, how to evaluate the video quality efficiently and accurately is essential in the fields of video processing, video quality monitoring and multimedia video applications. In this article, subjective, and comprehensive evaluation method of video quality were introduced, a video quality assessment system was completed, four ITU recommended videos were encoded and evaluated by Degradation Category Rating (DCR) and Structural Similarity (SSIM) methods using five different formats. After that, comprehensive evaluations with weights were applied. Results show that data of all three evaluations have good consistency; H.264 is the best encoding method, followed by Xvid and wmv8; the higher the encoding bit rate is, the better the evaluations are, but comparing to 1000kbps, the subjective and objective evaluation scores of 1400kbps couldn’t improve obviously. The whole process could also evaluate new encodings methods, and is applicable for high-definition video, finally plays a significant role in promoting the video quality evaluation and video encoding.

scholarly journals Design and Implementation of the HD Video Signal Converter Based on FPGA

2012 ◽  
Vol 6-7 ◽  
pp. 571-575
Author(s):  
Ling Fan Wu ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Kun Wang ◽  
Zhi Hui Deng
Keyword(s):  
Video Processing ◽  
Digital Video ◽  
Video Signal ◽  
High Definition ◽  
Standard Definition ◽  
Video Signals ◽  
Design And Implementation ◽  
Design Requirements ◽  
Signal Converter ◽  
Actual Test

In this paper, based on the FPGA and with a video dedicated A / D converter chip, LVDS coding chip, the design and implementation of a SD(standard-definition) analog video signals to HD(high-definition) digital video signal converter. First, input SD analog video into digital video signals meet the ITU-BT656 standard. Then use the FPGA with the video processing chip and DDR do some corresponding processing to achieve high-definition digital video output. After the actual test, the converter output signal of the image quality is well, meets the design requirements, and to verify the effectiveness of the program.

A Perceptually Optimized Foveation Wavelet Visible Difference Predictor Quality Metric Based on Psychovisual Properties of the Human Visual System (HVS)

2020 ◽  
pp. 268-281
Author(s):  
Abderrahim Bajit
Keyword(s):  
Visual System ◽  
Video Compression ◽  
Image Coding ◽  
Human Visual System ◽  
Quality Measurement ◽  
Region Of Interest ◽  
Visual Quality ◽  
Perceptual Quality ◽  
Foveated Imaging ◽  
Error Perception

Region of interest (ROI) image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Foveated imaging exploits the fact that the spatial resolution of the human visual system (HVS) is highest around the point of fixation (foveation point) and decreases dramatically with increasing eccentricity. Exploiting this fact, the authors have developed an appropriate metric for the assessment of ROI coded images, adapted to foveation image coding based on psycho-visual quality optimization tools, which objectively enable us to assess the visual quality measurement with respect to the region of interest (ROI) of the human observer. The proposed metric yields a quality factor called foveation probability score (FPS) that correlates well with visual error perception and demonstrating very good perceptual quality evaluation.

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 Video Quality Assessment through PSNR Estimation for Different Compression Standards

2018 ◽  
Vol 11 (3) ◽  
pp. 918 ◽  
Author(s):  
Renuka Girish Deshpande ◽  
Lata L Ragha ◽  
Satyendra Kumar Sharma
Keyword(s):  
Video Compression ◽  
Video Quality ◽  
Compressed Video ◽  
Video Traffic ◽  
Subjective Analysis ◽  
Video Signals ◽  
Interesting Task ◽  
Set Up ◽  
The Cost

<p align="center"><strong><em>Abstract</em></strong></p><p><em>           There is a threefold increase in video traffic over internet. Due to this video compression has become important. Compression of video signals is quiet an interesting task but comes at the cost of video quality. After compression, two methods are scientifically applied to evaluate the quality of video; Subjective and objective analysis. In subjective approach the compressed video is shown to a group of viewers and their feedback is recorded Objective approach aims to set up a mathematical model which can approximate the results of subjective analysis. One such approach is based on the measurement of PSNR. When a signal is applied to the encoder for compression, too much of compression results in a signal with a smaller size but at the same time quality of the signal degrades. In this paper we will compare the quality of compressed video signals produced by H.264, Mpeg2 and Mpeg4 encoder based on the values of MSE and PSNR. Lower the value of MSE, higher will be the PSNR. Comparative plots of MSE, PSNR, SSIM and images for subjective analysis have been added at the end of this paper. </em></p>

Design and performance of a digital video quality metric

1999 ◽  
Author(s):  
Andrew B. Watson ◽  
Quingmin J. Hu ◽  
John F. McGowan III ◽  
Jeffrey B. Mulligan
Keyword(s):  
Digital Video ◽  
Video Quality ◽  
Quality Metric ◽  
And Performance ◽  
Video Quality Metric

FPGA-Based ROI Encoding for HEVC Video Bitrate Reduction

2020 ◽  
Vol 29 (11) ◽  
pp. 2050182
Author(s):  
Zhilei Chai ◽  
Shen Li ◽  
Qunfang He ◽  
Mingsong Chen ◽  
Wenjie Chen
Keyword(s):  
Data Storage ◽  
Video Compression ◽  
High Efficiency ◽  
Video Quality ◽  
Region Of Interest ◽  
Absolute Difference ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
Mapping Algorithm ◽  
Block Based

The explosive growth of video applications has produced great challenges for data storage and transmission. In this paper, we propose a new ROI (region of interest) encoding solution to accelerate the processing and reduce the bitrate based on the latest video compression standard H.265/HEVC (High-Efficiency Video Coding). The traditional ROI extraction mapping algorithm uses pixel-based Gaussian background modeling (GBM), which requires a large number of complex floating-point calculations. Instead, we propose a block-based GBM to set up the background, which is in accord with the block division of HEVC. Then, we use the SAD (sum of absolute difference) rule to separate the foreground block from the background block, and these blocks are mapped into the coding tree unit (CTU) of HEVC. Moreover, the quantization parameter (QP) is adjusted according to the distortion rate automatically. The experimental results show that the processing speed on FPGA has reached a real-time level of 22 FPS (frames per second) for full high-definition videos ([Formula: see text]), and the bitrate is reduced by 10% on average with stable video quality.

scholarly journals Quality of Experience Assessment of Video Quality in Social Clouds

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Asif Ali Laghari ◽  
Hui He ◽  
Shahid Karim ◽  
Himat Ali Shah ◽  
Nabin Kumar Karn
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Service Providers ◽  
Video Quality ◽  
Original Video ◽  
File Size ◽  
High Definition ◽  
High Quality ◽  
Video Sharing

Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE) level of end users. To assess the QoE of video compression, we conducted subjective (QoE) experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.

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