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About 2,632 papers for “video preprocessing”

Video Preprocessing

Tong Lu, Palaiahnakote Shivakumara, Chew Lim Tan et al. · Advances in computer vision and pattern recognition · 2014

Cited by 2

Video Preprocessing For Audiovisual Indexing

Alberto Albiol, et al. · 2002

In this paper we address the problem of seeking anchor person shots in news sequences. This can be useful since usually this kind of scenes contain important and reusable information such as interviews. The proposed technique is based on our a priori knowledge of the editing techniques used in news sequences.

Cited by 0

Video preprocessing for audiovisual indexing

Alberto Albiol, L. Torres, Edward J. Delp · 2003

We address the problem of detecting shots of subjects that are interviewed in news sequences. This is useful since usually these kinds of scenes contain important and reusable information that can be used for other news programs. In a previous paper, we presented a technique based on a priori knowledge of the editing techniques used in news sequences which allowed a fast search of news stories (see Albiol, A. et al., 3rd Int. Conf. on Audio and Video-based Biometric Person Authentication, p.366-71, 2001). We now present a new shot descriptor technique which improves the previous search results by using a simple, yet efficient, algorithm, based on the information contained in consecutive frames. Results are provided which prove the validity of the approach.

Cited by 0

Video Preprocessing For Audiovisual Indexing

Alberto Albiol Politechnic, Alberto Albiol · 2002

In this paper we address the problem of reducing the amount of data to be analyzed when indexing audiovisual secuences. The reduction is based in selecting those parts of the video sequence where it is likely that there is a face talking. This can be useful since usually this kind of scenes contain important and reusable information such as interviews. The proposed technique is based on our a priori knowledge of the editing techniques used in news sequences. The results show that with this algorithm it is possible to discard arround of the news sequence with minimal processing. 1.

Cited by 0

Effect of preprocessing video frames.

Vincent On (2154274), Atena Zahedi (2154271), Iryna M. Ethell (222496) et al. · Figshare · 2017

<p>A) Temporal maximum intensity projection (TMIP) computed from an image sequence of dendrites. B) An original frame from the image sequence. C) An enhanced image generated by combining the original frame with the TMIP. D) Extracted foreground segmentation done on the original frame without TMIP. E) Foreground extracted after enhancement with TMIP. F) An overlay of foreground contours with (green outline) and without (red outline) TMIP on enhanced frame. This reveals that TMIP significantly improves segmentation of foreground.</p>

Cited by 0Open access

Video Results of visual preprocessing module

HU, Xiao · Figshare · 2021

These videos titled ellip-detect show the ellipse detection results. The results are obtained by further detecting ellipses on the patches detected by the visual preprocessing module. These videos demonstrate the developed arc-based ellipse detector can handle the occlusion very well and perform robustly in real conditions. These videos titled tracking show the visual tracking results. Object, after initialization by template matching, will not be re-detected again. So this is a one-passing evaluation. These videos titled detect show the results of the visual preprocessing module. Object, after initialization by template matching, will be re-detected again if tracking fails.

Cited by 0Open access

Adaptive High-Frequency Preprocessing for Video Coding

Yingxue Pang, Shijie Zhao, Junlin Li et al. · ArXiv.org · 2025

High-frequency components are crucial for maintaining video clarity and realism, but they also significantly impact coding bitrate, resulting in increased bandwidth and storage costs. This paper presents an end-to-end learning-based framework for adaptive high-frequency preprocessing to enhance subjective quality and save bitrate in video coding. The framework employs the Frequency-attentive Feature pyramid Prediction Network (FFPN) to predict the optimal high-frequency preprocessing strategy, guiding subsequent filtering operators to achieve the optimal tradeoff between bitrate and quality after compression. For training FFPN, we pseudo-label each training video with the optimal strategy, determined by comparing the rate-distortion (RD) performance across different preprocessing types and strengths. Distortion is measured using the latest quality assessment metric. Comprehensive evaluations on multiple datasets demonstrate the visually appealing enhancement capabilities and bitrate savings achieved by our framework.

Cited by 0Open access

Human Perception based Video Preprocessing for Telesurgery

Jian Xu, Robert J. Sclabassi, Qiang Liu et al. · Conference proceedings · 2007

Video transmission plays a critical role in robotic telesurgery because of the required high bandwidth and quality requirement. We propose an adaptive video preprocessing technique to accelerate the transmission of telesurgical video. Using this technique, the bandwidth can be reallocated adaptively from non-essential surrounding regions to the region of interest when preprocessed image sequences are passed to the video encoder, ensuring excellent image quality of critical regions (e.g. surgical landscape). The algorithm first generates a physiologically appropriate attention map which can be updated dynamically. Then, an adaptive edge-preserving image smoothing filter is utilized to remove insignificant features. The parameters of the filter are adjusted according to the attention map. Our experimental result shows that, with the preprocessing technique, over half of the bandwidth can be reduced while there is no significant visual effect at the site of remote observer.

Cited by 5

A tool for automatic preprocessing stereoscopic-video

Norbert Blenn, N. V. Festenberg, Marcel Spehr et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010

Stereoscopic movie production has been a topic in the making of films for a long time. However, it hasn't made it to the amateur sector. Commercially available stereoscopic cameras are too expensive for non professionals. When producing a stereo video with two separate standard cameras, synchronicity and spatial offset maintenance between the two views is a challenging non-trivial task. Even when this is done properly, the general lack of software-tools for stereo video post production definitely daunts any non-professional ambitions. In this work we present a tool for preprocessing stereoscopic videos. We describe how the input videos can be converted automatically to a stereo-movie, ready for displaying or further processing in standard video software.

Cited by 1

GOP-Based Deep Preprocessing for Video Coding

Daichi Arai, Shunsuke Iwamura, Kazuhisa Iguchi et al. · 2024

Neural network-based video preprocessing techniques have recently shown remarkable improvements in video codec performance. However, conventional preprocessing methods tend to prioritize perceptual quality over peak signal-to-noise ratio (PSNR), a key standard for video quality assessment. In this study, We propose a novel deep preprocessing method based on a group of pictures (GOP) structure, specifically aimed at enhancing the rate-distortion performance in terms of PSNR. This approach involves developing a video compression model that employs the GOP structure of the target video codec and training a preprocessing model through joint optimization with the video compression model. Experimental results demonstrate that our GOP-based deep preprocessing method not only improves PSNR but also elevates other quality metrics, including VMAF, across various codecs like MPEG-2, HEVC, and VVC. Additionally, ablation studies highlight the critical role of GOP structures in enhancing encoding efficiency based on PSNR.

Cited by 2

Audio/Video Fusion: a Preprocessing Step for

Multimodal Person Identification · 2008

In the audiovisual indexing context, we propose a system for automatic association of voices and images. This association can be used as a preprocessing step for existing applications like person identification systems. We use a fusion of audio and video indexes (without any prior knowledge) in order to make the information brought by each of them more robust. If both audio and video indexes are correctly segmented, this automatic association yields excellent results. In order to deal with oversegmentation, we propose an approach which uses one index to improve the segmentation of the other. We show that the use of the audio index improves an oversegmented video index on a corpus composed of French TV broadcasts. 1

Cited by 0

Color Correction Preprocessing for Multiview Video Coding

Colin Doutre, Panos Nasiopoulos · IEEE Transactions on Circuits and Systems for Video Technology · 2009

In multiview video, a number of cameras capture the same scene from different viewpoints. There can be significant variations in the color of views captured with different cameras, which negatively affects performance when the videos are compressed with inter-view prediction. In this letter, a method is proposed for correcting the color of multiview video sets as a preprocessing step to compression. Unlike previous work, where one of the captured views is used as the color reference, we correct all views to match the average color of the set of views. Block-based disparity estimation is used to find matching points between all views in the video set, and the average color is calculated for these matching points. A least-squares regression is performed for each view to find a function that will make the view most closely match the average color. Experimental results show that when multiview video is compressed with joint multiview video model, the proposed method increases compression efficiency by up to 1.0 dB in luma peak signal-to-noise ratio (PSNR) compared to compressing the original uncorrected video.

Cited by 57

Video Preprocessing for American Football Formation Recognition

Kimi Wright, Shad Torrie, Benjamin Orr et al. · 2024

American football analytics and in particular formation identification is important to gaining a competitive edge. Currently, most of this football video annotation process is done by hand. With the recent advancements in computer vision and artificial intelligence, automated sport analysis systems are gaining popularity. One major challenge in current football systems is constraints on camera location. In this work we attempt to overcome issues of camera location by performing a perspective normalization method. This entails using conventional computer vision algorithms to locate various lines and markers on the football field. This information is then used to normalize points into a camera position agnostic perspective. This preprocessing will then allow for a neural network to be trained to identify formations and preform other football analytics.

Cited by 4

Optimization of DeepFake Video Detection Using Image Preprocessing

Ali Berjawi, Khouloud Samrouth, Olivier Déforges · 2023

Deep learning has been evolving recently which allowed it to handle complex problems like big data, computer vision, and human-level control. One of the deep learning-powered applications recently emerged is called “deepfake”. Deepfake algorithms have recently been a controversial development in Artificial Intelligence, because they use deep learning to generate fake yet realistic content based on an input dataset. As a result, many are concerned with the potential risks in terms of cyber-security as it causes threats to privacy, democracy, and national security. Multiple techniques were proposed to detect deepfake videos, however most cannot cope with the variety of the deepfake generation techniques. Therefore, in this study, we optimize one of the best existing deepfake detection methods based on Xception model. In particular, our proposed optimization scheme consists of a pre-processing phase performing advanced image enhancement on the videos in hand for highlighting the face features for better feature extraction as well fake content detection, which is preceded by a close-up dataset cleansing. Our experiments show that the proposed pre-processing optimization scheme had improvemes the performance of the Xception Binary Classifier- Inference model from 94% to 96%.

Cited by 6

Preprocessing Video Images for Neural Learning of Lipreading

K. Venkatesh Prasad, David G. Stork, Gregory J. Wolff · 1993

We present new procedures for preprocessing video images for automatic lipreading applications. Prepro-cessing consists of first detecting the region of interest (ROl) using the eyes, nostrils and mouth followed by estimating salient features within the ROl. Our algorithms for ROT detection employ a low-pass Gaussian filter followed by a gray level thresholding and are talker invariant and quite robust to variations in overall head size, orientation and lighting conditions. Subsequent feature extraction is based on both spatial and temporal information associated with luminance histogram profiles within the ROT. For instance, the tongue tip (e.g., in /la/) is detected by counting changes in the number of minima in the luminance profile of a vertical band passing over the mouth region. A neural system learns hidden unit representations which encode predictive groupings of these features. Even though our overall system employs both visual and acoustic information for speech recognition, the visual features employed are sufficiently predictive that under certain restricted domains our system can recognize speech even in the absence of acoustic information. We illustrate our algorithms using digitized video sequences of human faces, and conclude by describing real-world applications for computer lipreading systems, such as the high noise environments of factory floors, large offices, automobile interiors and crowded city streets. The problems we address

Cited by 0

A Comparative Preprocessing Study for SoftCast Video Transmission

Anthony Trioux, François‐Xavier Coudoux, Patrick Corlay et al. · 2018

An original wireless video transmission scheme called SoftCast has been recently proposed to deal with the issues encountered in conventional wireless video broadcasting systems such as cliff effect. In this paper, we first review the SoftCast scheme. We then analyze and compare two simple preprocessing methods that help to increase the quality at the receiver side. Each method consists of the subtraction of either the 8-bit mean gray level 128 or the mean value of all pixels for each video frame. Preprocessing methods are compared and evaluated under different channel signal-to-noise ratios using two metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM). Simulation results clearly highlight the importance of the preprocessing block in a SoftCast wireless video transmission scheme. Depending on the video input characteristics, an improvement of the PSNR score up to 2.1dB and 2.5dB can be observed for the average gray level method and for the mean frame method, respectively.

Cited by 7