Video coding with dynamic background

Paul, Manoranjan; Wang, Lin; Lau, Chiew Tong; Lee, Bu-Sung

doi:10.1186/1687-6180-2013-11

Cited by 23 publications

(13 citation statements)

References 26 publications

(68 reference statements)

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“…This relationship is also observed by the other researchers, and thus, pre-/post-filtering techniques were introduced by exploiting neighboring pixels to reduce pixel intensity discrepancy in decoded frames due to the quantization and/or block-based ME&MC [31,32]. Paul et al in [33] generated McFIS with modified decoded frames using neighboring pixel intensities of the decoded …”

Section: New Mcfis Generation Techniquementioning

confidence: 90%

Pattern-based video coding with dynamic background modeling

Paul

Wang

Lau

et al. 2013

EURASIP J. Adv. Signal Process.

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The existing video coding standard H.264 could not provide expected rate-distortion (RD) performance for macroblocks (MBs) with both moving objects and static background and the MBs with uncovered background (previously occluded). The pattern-based video coding (PVC) technique partially addresses the first problem by separating and encoding moving area and skipping background area at block level using binary pattern templates. However, the existing PVC schemes could not outperform the H.264 with significant margin at high bit rates due to the least number of MBs classified using the pattern mode. Moreover, both H.264 and the PVC scheme could not provide the expected RD performance for the uncovered background areas due to the unavailability of the reference areas in the existing approaches. In this paper, we propose a new PVC technique which will use the most common frame in a scene (McFIS) as a reference frame to overcome the problems. Apart from the use of McFIS as a reference frame, we also introduce a content-dependent pattern generation strategy for better RD performance. The experimental results confirm the superiority of the proposed schemes in comparison with the existing PVC and the McFIS-based methods by achieving significant image quality gain at a wide range of bit rates.

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Section: New Mcfis Generation Techniquementioning

confidence: 90%

Pattern-based video coding with dynamic background modeling

Paul

Wang

Lau

et al. 2013

EURASIP J. Adv. Signal Process.

Self Cite

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“…In ASSC-DNL Technique, Detection Accuracy ' ' is determined as the relative amount of moving objects that are precisely detected as, (20) With reference to the (20), the correctness of moving objects detection in a video sequence is estimated with respect to a varied number of video frames. Here, ' ' denotes video frames, 'k' refers the overall video frames within the simulation work.…”

Section: Detection Accuracymentioning

confidence: 99%

Anisotropic Sophisticated Spatio-Temporal Contours Based Deep Neural Learned Moving Objects Detection in Video

Kader*,

Nihal

2019

IJRTE

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Object detection in the video sequence is a significant problem to be resolved in image processing because it used different applications in video compression, video surveillance, robot technology, etc. Few research works have been designed in conventional works to discover moving objects using various machine learning techniques. However, dynamic changing background, object size variations and degradation of video frames during the object detection process remained an open issue. In order to overcome such limitations, Anisotropic Sophisticated Spatiotemporal Contours based Deep Neural Network Learning (ASSC-DNNL) practice is projected. ASSC-DNL Technique initially obtains a number of video file as input at the input layer. After acquiring the video, input layer forward it to hidden layers. Subsequently, ASSC-DNL Technique accomplishes the encoding process in the first hidden layer using Anisotropic Stacked Autoencoder (ASA). During the encoding process, ASSC-DNL practice maps each video frames pixels in input video via code. This practice results in compressed video with enhanced quality. Afterward, ASSC-DNL practice transforms compressed video into a numeral of frames in the second concealed layer. Followed by, ASSC-DNL practice carried out Teknomo–Fernandez Spatiotemporal Based Background Subtraction (TS-BS) process at the third hidden layer, in which it effectively segments the foreground images from dynamic changing background. Then, ASSC-DNL practice deep analyzes the foreground image of video frames and mines some features like shape, color, texture, intensity, and size. Finally, ASSC-DNL Technique exactly finds the moving objects in video frames according to identified features with minimal time at the output layer. Therefore, ASSC-DNL Technique obtains enhanced moving objects detection performance when compared to existing works. The simulation of ASSC-DNL practice is conducted via different metrics such as accuracy, time and false positive rate towards in detection.

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“…A learning parameter 1 . 0   [30] is used to balance the contribution of the current and past values of parameters such as weight, variance, mean, etc. Then  / 1 defines the time constant which determines the speed at which the distribution's parameters change.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

“…Then  / 1 defines the time constant which determines the speed at which the distribution's parameters change. The system starts with an empty set of models and then for every new observation t Z at the current time t, it is first matched against the existing models in order to find one (say the l-th) such that  if it exists [30].…”

Section: Motivations and Contributionsmentioning

confidence: 99%