scholarly journals An Efficient Hierarchical Video Coding Scheme Combining Visual Perception Characteristics

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Pengyu Liu ◽  
Kebin Jia
Keyword(s):  
Visual Perception ◽  
Video Coding ◽  
Priority Setting ◽  
Motion Vector ◽  
Region Of Interest ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Coding Scheme ◽  
Encoding Method ◽  
Perception Analysis

Different visual perception characteristic saliencies are the key to constitute the low-complexity video coding framework. A hierarchical video coding scheme based on human visual systems (HVS) is proposed in this paper. The proposed scheme uses a joint video coding framework consisting of visual perception analysis layer (VPAL) and video coding layer (VCL). In VPAL, effective visual perception characteristics detection algorithm is proposed to achieve visual region of interest (VROI) based on the correlation between coding information (such as motion vector, prediction mode, etc.) and visual attention. Then, the interest priority setting for VROI according to visual perception characteristics is completed. In VCL, the optional encoding method is developed utilizing the visual interested priority setting results from VPAL. As a result, the proposed scheme achieves information reuse and complementary between visual perception analysis and video coding. Experimental results show that the proposed hierarchical video coding scheme effectively alleviates the contradiction between complexity and accuracy. Compared with H.264/AVC (JM17.0), the proposed scheme reduces 80% video coding time approximately and maintains a good video image quality as well. It improves video coding performance significantly.

scholarly journals Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Pengyu Liu ◽  
Kebin Jia
Keyword(s):  
Visual Perception ◽  
Video Coding ◽  
Saliency Detection ◽  
Motion Vector ◽  
Region Of Interest ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Characteristics Analysis ◽  
Perceptual Video Coding ◽  
Saliency Region

A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding.

A Low Complexity Multi-View Video Coding Scheme for Wireless Video Sensor Array

2008 ◽  
Vol 28 (1) ◽  
pp. 62-66 ◽  
Author(s):  
蒋刚毅 Jiang Gangyi ◽  
姜浩 Jiang Hao ◽  
郁梅 Yu Mei ◽  
蒋志迪 Jiang Zhidi ◽  
刘尉悦 Liu Weiyue
Keyword(s):  
Video Coding ◽  
Sensor Array ◽  
Low Complexity ◽  
Wireless Video ◽  
Coding Scheme ◽  
Video Sensor

scholarly journals Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1121 ◽  
Author(s):  
Nassr Alsaeedi ◽  
Dieter Wloka
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Vector ◽  
Region Of Interest ◽  
Video Stream ◽  
Detection Algorithm ◽  
Case Scenario ◽  
Current Frame ◽  
Eyelid Closure ◽  
And Performance

The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies.

Low-Complexity Encoding in Block-Based Hybrid Video Codecs by Moving Motion Estimation to Decoder Side

2014 ◽  
Vol 5 (1) ◽  
pp. 19-29
Author(s):  
J. Karlsson
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Low Complexity ◽  
Motion Prediction ◽  
Motion Vectors ◽  
Video Codecs ◽  
Coding Efficiency ◽  
Coding Scheme ◽  
Network Delays ◽  
Block Based

In this paper the authors present an approach to provide efficient low-complexity encoding for the block-based video coding scheme. The authors present a method based on removing the most time-consuming task, that is motion estimation, from the encoder. Instead the decoder will perform motion prediction based on the available decoded frame and send the predicted motion vectors to the encoder. The results presented are based on a modified H.264 implementation. The results show that this approach can provide rather good coding efficiency even for relatively high network delays.

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