Toward developing a unit of measure and scale of digital video quality: IEEE Broadcast Technology Society Subcomittee on Video Compression Measurements

2000 ◽  
Author(s):  
John M. Libert ◽  
Leon Stanger ◽  
Andrew B. Watson ◽  
Ann M. Rohaly
Keyword(s):  
Video Compression ◽  
Digital Video ◽  
Video Quality

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 Impact of Scene Content on High Resolution Video Quality

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2872
Author(s):  
Miroslav Uhrina ◽  
Anna Holesova ◽  
Juraj Bienik ◽  
Lukas Sevcik
Keyword(s):  
Video Compression ◽  
Spatial Information ◽  
Video Quality ◽  
Video Sequences ◽  
Compression Efficiency ◽  
Opinion Score ◽  
Scene Description ◽  
Two Parameters ◽  
The University ◽  
The Impact

This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.

Fractal Coding Based Video Compression Using Weighted Finite Automata

2018 ◽  
Vol 9 (1) ◽  
pp. 115-133 ◽  
Author(s):  
Shailesh D. Kamble ◽  
Nileshsingh V. Thakur ◽  
Preeti R. Bajaj
Keyword(s):  
Video Compression ◽  
Signal To Noise Ratio ◽  
Finite Automata ◽  
Video Quality ◽  
Self Similarity ◽  
Fractal Coding ◽  
Time Compression ◽  
Practical Reality ◽  
Weighted Finite Automata ◽  
Evaluation Parameters

Main objective of the proposed work is to develop an approach for video coding based on Fractal coding using the weighted finite automata (WFA). The proposed work only focuses on reducing the encoding time as this is the basic limitation why the Fractal coding not becomes the practical reality. WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image based on the idea of fractal that the image has self-similarity in itself. The plane WFA (applied on every frame), and Plane FC (applied on every frame) coding approaches are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, Traffic, Paris, Bus, Akiyo, Mobile, Suzie etc. and on the recorded video, namely, Geometry and Circle. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio, compression percentage, bits per pixel and Peak Signal to Noise Ratio (PSNR). Though the initial number of states is 256 for every frame of all the types of videos, but we got the different number of states for different frames and it is quite obvious due to minimality of constructed WFA for respective frame. Based on the obtained results, it is observed that the number of states is more in videos namely, Traffic, Bus, Paris, Mobile, and Akiyo, therefore the reconstructed video quality is good in comparison with other videos namely, Circle, Suzie, and Geometry.

Secure Information Delivery through High Bitrate Data Embedding within Digital Video and its Application to Audio/Video Synchronization

2012 ◽  
Vol 6 (4) ◽  
pp. 71-93
Author(s):  
Ming Yang ◽  
Chih-Cheng Hung ◽  
Edward Jung
Keyword(s):  
Video Compression ◽  
Information Hiding ◽  
Secure Communication ◽  
Digital Video ◽  
Lossy Compression ◽  
Data Encryption ◽  
Practical Applications ◽  
Secure Information ◽  
Video Synchronization ◽  
Audio Video

Secure communication has traditionally been ensured with data encryption, which has become easier to break than before due to the advancement of computing power. For this reason, information hiding techniques have emerged as an alternative to achieve secure communication. In this research, a novel information hiding methodology is proposed to deliver secure information with the transmission/broadcasting of digital video. Secure data will be embedded within the video frames through vector quantization. At the receiver end, the embedded information can be extracted without the presence of the original video contents. In this system, the major performance goals include visual transparency, high bitrate, and robustness to lossy compression. Based on the proposed methodology, the authors have developed a novel synchronization scheme, which ensures audio/video synchronization through speech-in-video techniques. Compared to existing algorithms, the main contributions of the proposed methodology are: (1) it achieves both high bitrate and robustness against lossy compression; (2) it has investigated impact of embedded information to the performance of video compression, which has not been addressed in previous research. The proposed algorithm is very useful in practical applications such as secure communication, captioning, speech-in-video, video-in-video, etc.

scholarly journals Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

2020 ◽  
Vol 10 (5) ◽  
pp. 1793
Author(s):  
Lina Du ◽  
Li Zhuo ◽  
Jiafeng Li ◽  
Jing Zhang ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Video Compression ◽  
Quality Of Experience ◽  
Feature Vector ◽  
Video Quality ◽  
Superior Performance ◽  
Adaptive Streaming ◽  
Input Feature ◽  
Streaming Services ◽  
Content Characteristics

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.

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