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.

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 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.

scholarly journals Low-complexity motion-based saliency map estimation for perceptual video coding

CONATEL 2011 ◽  
2011 ◽  
Author(s):  
Ana Belen Mejia-Ocana ◽  
Manuel de-Frutos-Lopez ◽  
Sergio Sanz-Rodriguez ◽  
Oscar del-Ama-Esteban ◽  
Carmen Pelaez-Moreno ◽  
...  
Keyword(s):  
Video Coding ◽  
Low Complexity ◽  
Saliency Map ◽  
Map Estimation ◽  
Perceptual Video Coding

Low-Complexity Soft-ML Detection Algorithm for Modified-DCM in WiMedia UWB Systems

2013 ◽  
Vol E96.B (3) ◽  
pp. 910-913 ◽  
Author(s):  
Kilhwan KIM ◽  
Jangyong PARK ◽  
Jihun KOO ◽  
Yongsuk KIM ◽  
Jaeseok KIM
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Ml Detection

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Low Complexity Trellis-Coded Quantization in Versatile Video Coding

2021 ◽  
Vol 30 ◽  
pp. 2378-2393
Author(s):  
Meng Wang ◽  
Shiqi Wang ◽  
Junru Li ◽  
Li Zhang ◽  
Yue Wang ◽  
...  
Keyword(s):  
Video Coding ◽  
Low Complexity ◽  
Trellis Coded
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