Original Video Signal Research Articles

A temporal shift reconstruction network for compressive video sensing

AbstractCompressive sensing provides a promising sampling paradigm for video acquisition for resource‐limited sensor applications. However, the reconstruction of original video signals from sub‐sampled measurements is still a great challenge. To exploit the temporal redundancies within videos during the recovery, previous works tend to perform alignment on initial reconstructions, which are too coarse to provide accurate motion estimations. To solve this problem, the authors propose a novel reconstruction network, named TSRN, for compressive video sensing. Specifically, the authors utilise a number of stacked temporal shift reconstruction blocks (TSRBs) to enhance the initial reconstruction progressively. Each TSRB could learn the temporal structures by exchanging information with last and next time step, and no additional computations is imposed on the network compared to regular 2D convolutions due to the high efficiency of temporal shift operations. After the enhancement, a bidirectional alignment module to build accurate temporal dependencies directly with the help of optical flows is employed. Different from previous methods that only extract supplementary information from the key frames, the proposed alignment module can receive temporal information from the whole video sequence via bidirectional propagations, thus yielding better performance. Experimental results verify the superiority of the proposed method over other state‐of‐the‐art approaches quantitatively and qualitatively.

RT-BVE—Real-Time no-reference Blocking Visibility Estimation in video frames

During lossy video compression, a blocking artifact is introduced into a video. It can significantly reduce the quality of a compressed video, and consequently end-user Quality of Experience. Hence, it is very important to measure and monitor the quality of compressed videos delivered to the end-user, which can be performed only by using the no-reference (NR) approach, without the original video signal available. Many NR video quality assessment (VQA) metrics are based on measurements of different artifacts visibility, which are then used as an input for an objective NR VQA metric. Thus, reliable artifacts detection and their visibility estimation are the first step in a reliable VQA process. In this paper, a new algorithm for Real-Time NR Blocking Visibility Estimation (RT-BVE) in video frames is proposed. The performance of the proposed RT-BVE algorithm is compared to the performance of two freely available algorithms, i.e., BBT-BDA and MSU Blocking, using videos from the well-known databases (156 video sequences in total and more than 48,000 frames). While RT-BVE achieves performance comparable to BBT-BDA and MSU Blocking algorithms for MPEG-2 videos, it outperforms both algorithms for H.264 videos. The results show that the proposed RT-BVE algorithm can precisely estimate blocking visibility in video frames for videos compressed according to different compression standards by using a fixed set of algorithm parameter values. Additionally, RT-BVE is suitable for usage in real-time applications since it is capable of processing approximately 100 Full HD video frames per second on the current midrange × 86–64 platform.

The Algorithm for Compensating Distortion of Images of Type “Blur”

The paper considers the model of linear blurring with a uniform rectilinear motion of an object when its speed is constant, and its direction is parallel to the lines of the formed image. An algorithm for linear blurring compensation based on the account of the physical processes of its formation when obtaining the images is proposed. A system of equations for the restoration of the original video signal samples undistorted image of the object is obtained. Processed the frame of a real video sequence with a horizontal blur and presented its results. Considered the comparison of the results of the restoration of distorted images by the proposed algorithm with known methods.

Distributed video coding of secure compressed sensing

AbstractIn this paper, we use the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, we use the quantum‐behaved particle swarm optimization algorithm to reconstruct video signal. In this paper, we analyze the computational complexity of two algorithms, simulation results demonstrate that quantum‐behaved particle swarm optimization algorithm greatly reduce the amount of computation. And it has better stability and faster convergence to deal with original video signal. Simulation results demonstrate that we can obtain the better reconstructed video with low sample value, and it can guarantee safety performance. Copyright © 2013 John Wiley & Sons, Ltd.

Retinex based visual identicalness detection for videos corrupted by imaging noise

Detecting the visually identical regions among successive frames for noisy videos, called visual identicalness detection (VID) in this paper, is a fundamental tool in video applications for lower power consumption and higher efficiency. In this paper, instead of performing VID on the original video signal or on the de-noised video signal, a Retinex based VID approach is proposed to perform VID on the Retinex signal to eliminate the noise influence introduced by imaging system. Several Retinex output generation approaches are compared, within which the proposed Cohen–Daubechies–Feauveau wavelet based approach is demonstrated to have better efficiency in detection and higher adaptability to the video content and noise severity. Compared with approaches performing detection in the de-noised images, the proposed algorithm presents up to 4.78 times higher detection rate for the videos with moving objects and up to 30.79 times higher detection rate for the videos with static scenes, respectively, at the same error rate. Also, an application of this technique is provided by integrating it into an H.264/AVC video encoder. Compared with compressing the de-noised videos using the existing fast algorithm, an average of 1.7dB performance improvement is achieved with up to 5.47 times higher encoding speed. Relative to the reference encoder, up to 32.47 times higher encoding speed is achieved without sacrificing the subjective quality.

Kalman Filtering Technique For Video Denoising Method

Digital Cameras which capture images and videos are directly in digital form .Digital Images or Videos are often corrupted by impulse noises. It is caused by disturbances and corrupted in the video signal. So the image processing scheme should be one of the important part in any vision application permitting to suppress noise and improve the image performances. This demands to have number of filtering schemes are introduced such as fuzzy and nonfuzzy and linear and non-linear are used. In this paper, propose Kalman filter is used to remove the impulse noise. Kalman filter is the best and efficient filters in the sense of minimizing mean square error (MSE) and high PSNR (peak signal to noise ratio) between the original video signal and recovered video signal.

Video Denoising Based on a Spatiotemporal Gaussian Scale Mixture Model

We propose a video denoising algorithm based on a spatiotemporal Gaussian scale mixture model in the wavelet transform domain. This model simultaneously captures the local correlations between the wavelet coefficients of natural video sequences across both space and time. Such correlations are further strengthened with a motion compensation process, for which a Fourier domain noise-robust cross correlation algorithm is proposed for motion estimation. Bayesian least square estimation is used to recover the original video signal from the noisy observation. Experimental results show that the performance of the proposed approach is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

Exploiting quantization and spatial correlation in virtual-noise modeling for distributed video coding

Aiming for low-complexity encoding, video coders based on Wyner–Ziv theory are still unsuccessfully trying to match the performance of predictive video coders. One of the most important factors concerning the coding performance of distributed coders is modeling and estimating the correlation between the original video signal and its temporal prediction generated at the decoder. One of the problems of the state-of-the-art correlation estimators is that their performance is not consistent across a wide range of video content and different coding settings. To address this problem we have developed a correlation model able to adapt to changes in the content and the coding parameters by exploiting the spatial correlation of the video signal and the quantization distortion. In this paper we describe our model and present experiments showing that our model provides average bit rate gains of up to 12% and average PSNR gains of up to 0.5 dB when compared to the state-of-the-art models. The experiments suggest that the performance of distributed coders can be significantly improved by taking video content and coding parameters into account.

Intermediate description for multiple video adaptation

Video adaptation has been considered as a promising technique to tackle challenging problems in pervasive media applications. However, the style of video representation and description in current framework is too simple to accommodate diversified usages, especially for realtime application environment. In this paper, an intermediate video description termed intermedia is proposed, which is generated by off-line pre-processing the original video signal and includes abundant information of video content. On the basis of intermedia, a novel framework for video adaptation can be implemented to handle multiple video applications. The framework has low complexity operations not only in signal level, but also in structural level, and in joint level. Texture, motion and structural characteristic are included in intermedia, so that the framework can tackle a number of different adaptation applications, e.g., bit-rate conversion, temporal resolution reduction, video summarization browsing, and multi-dimensional adaptation. Experimental results demonstrate that intermedia can support multiple video adaptations rapidly while maintaining coding efficiency.

Functionally Layered Video Coding for Water Level Monitoring

This paper proposes a new type of layered video coding especially for the use of monitoring water level of a river. A sensor node of the system decomposes an input video signal into some kinds of component signals and produces a bit stream functionally separated into three layers. The first layer contains the minimum components effective for detecting the water level. It is transmitted at very low bit rate for regular monitoring. The second layer contains signals for thumb-nail video browsing. The third layer contains additional data for decoding the original video signal. These are transmitted in case of necessity. A video signal is decomposed into several bands with the three dimensional Haar transform. In this paper, optimum bands to be contained into the 1st layer are experimentally investigated considering both of water level detection and data size to be transmitted. As a result, bit rate for transmitting the first layer is reduced by 32.5% at the cost of negligible 3.7% decrease of recognition performance for one of video examples.

Systematic Lossy Error Protection based on H.264/AVC redundant slices and flexible macroblock ordering

The authors propose a scheme for Systematic Lossy Error Protection (SLEP) of an H.264/AVC compressed video bit stream, using standard compatible features such as redundant slices, and flexible macroblock ordering. The systematic portion consists of a conventional H.264/AVC bit stream. For error resilience, an additional Wyner-Ziv bit stream is also transmitted. The Wyner-Ziv bit stream allows the decoding of a coarsely quantized description of the original video signal, and is efficiently gen- erated by using H.264/AVC redundant slices in conjunction with Reed-Solomon coding. The Wyner-Ziv bit stream is decoded in order to recover the redundant video descriptions, which are used in lieu of portions lost from the original video signal due to channel errors. SLEP allows the video quality to degrade gracefully with worsening channel conditions, and provides a flexible trade-off between the achieved error resilience and the coarseness of the redundant description. The performance can be improved especially for low motion video sequences, by applying SLEP to a region-of-interest in the video frame, using flexible macroblock ordering (FMO). Experimental results provided for two video transmission scenarios, demonstrate the advantages of SLEP over forward error correction (FEC) as an error resilience scheme.

An adaptive order-statistic noise filter for gamma-corrected image sequences

Original video signals are often corrupted by a certain amount of noise originating from the camera electronics. As a result of the gamma correction in cameras, the observed noise is signal dependent. We present a spatio-temporal order-statistic (OS) noise filter that takes into account the gamma correction in the camera. The calculation of the filter coefficients requires higher-order order-statistics (HOOS) of the noise process. We make use of a range test (RT) to determine locally from which neighboring signal values an estimate should be formed. The noise filter that we arrive at is adaptive and computationally efficient.

Embedding Data Into Pictures by Modulo Masking

Three systems are proposed for embedding data into industrial quality monochrome analog pictures. The video signal on each scan line is sampled, and a data bit is inserted into a block of three or five pels by modulo masking scrambling the luminance level of only one pel in the block. Prior to transmission, the combined data and video sequence is converted into a continuous signal with a bandwidth that is no greater than that of the original video signal. Using six images each containing 65 536 pels, Systems 1 and 2 embedded an average of 17 430 and 8713 bits per image, while System 3 accommodated data at a constant rate of 21 760 bits/image. The data embedding procedures of Systems 1, 2, and 3 operated with average picture SNR's of 41, 44, and 30 dB, respectively, when the transmission channel was ideal. When the transmission was over a channel composed of a second-order Butterworth filter plus additive noise that yield a channel SNR of 40 dB, no bit errors occurred but System 3 offered the greater safety margin to bit errors than Systems 1 and 2.

Synthetic Highs — An Experimental TV Bandwidth Reduction System

A complete system is described which codes a standard video signal to match a narrower band channel and subsequently decodes the received signal for display on a standard TV monitor. The system transmits the low-frequency, or macro-contrast signal, in analog form. The location and amplitude of the edges are transmitted by a digital code. At the receiver the edge information is used to synthesize the highs content of the video, which is then added to the lows to reconstruct the original video signal. Bandwidth reduction is achieved by exploiting both statistical correlations and psychophysical phenomena. — Apparatus for the separation of low frequencies, detection of edges, quantization and binary digital coding of edge amplitude and synthetic reconstruction of highs is described. The digital coder for edge locations has been described elsewhere. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Picture-tube photographs of the resulting pictures are shown. Factors affecting the degree of bandwidth reduction and the effect of variation of system parameters such as separation frequency and quantization levels are discussed.