Context-Adaptive Inverse Quantization for Inter-Frame Coding

To create a realistic 3D perception on glasses-free displays, it is critical to support continuous motion parallax, greater depths of field, and wider fields of view. A new type of Layered or Tensor light field 3D display has attracted greater attention these days. Using only a few light-attenuating pixelized layers (e.g., LCD panels), it supports many views from different viewing directions that can be displayed simultaneously with a high resolution. This paper presents a novel flexible scheme for efficient layer-based representation and lossy compression of light fields on layered displays. The proposed scheme learns stacked multiplicative layers optimized using a convolutional neural network (CNN). The intrinsic redundancy in light field data is efficiently removed by analyzing the hidden low-rank structure of multiplicative layers on a Krylov subspace. Factorization derived from Block Krylov singular value decomposition (BK-SVD) exploits the spatial correlation in layer patterns for multiplicative layers with varying low ranks. Further, encoding with HEVC eliminates inter-frame and intra-frame redundancies in the low-rank approximated representation of layers and improves the compression efficiency. The scheme is flexible to realize multiple bitrates at the decoder by adjusting the ranks of BK-SVD representation and HEVC quantization. Thus, it would complement the generality and flexibility of a data-driven CNN-based method for coding with multiple bitrates within a single training framework for practical display applications. Extensive experiments demonstrate that the proposed coding scheme achieves substantial bitrate savings compared with pseudo-sequence-based light field compression approaches and state-of-the-art JPEG and HEVC coders.

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Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Sensors ◽

10.3390/s21123953 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3953

Author(s):

Han Pu ◽

Tianqiang Huang ◽

Bin Weng ◽

Feng Ye ◽

Chenbin Zhao

Keyword(s):

Detection Method ◽

Real Life ◽

Vital Role ◽

Video Forensics ◽

Flow Algorithm ◽

Benchmark Datasets ◽

Media Reports ◽

Intensity Normalization ◽

Inter Frame ◽

Stable Feature

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

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Binomial Bit-Rate Computation Model Based on Wireless Channel

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.2482 ◽

2012 ◽

Vol 241-244 ◽

pp. 2482-2486

Author(s):

Wei Ming Yang ◽

Jian Zhang ◽

Jin Xiang Peng

Keyword(s):

Solution Method ◽

Rate Distortion ◽

Wireless Channel ◽

Video Communication ◽

Bit Allocation ◽

Bit Rate ◽

Computation Model ◽

Refresh Rate ◽

Network Bandwidth ◽

Inter Frame

For the encoding bit-rate problem in H.264 wireless video communication, the bit-rate computation model and the standard deviation distortion model were analyzed to establish the relation between the quantization parameter of encoding bit-rate and the intra-frame refresh rate of macroblocks, a new proposal of the coding rate thus put forward based on the general binomial computation model theory. Furthermore, this method not only can adaptively adjust the bit allocation and quantization parameters to prevent buffer from overflowing downward or upward under given network bandwidth, but also can apply the rate-distortion to perfect the solution method, control the encoding bits accurately and optimize the allocation between the inter-frame encoding macroblocks.

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Unsupervised Learning from Videos for Object Discovery in Single Images

Symmetry ◽

10.3390/sym13010038 ◽

2020 ◽

Vol 13 (1) ◽

pp. 38

Author(s):

Dong Zhao ◽

Baoqing Ding ◽

Yulin Wu ◽

Lei Chen ◽

Hongchao Zhou

Keyword(s):

Input Image ◽

Frame Structure ◽

Object Discovery ◽

Spatial Transformations ◽

Learning Tasks ◽

Video Frames ◽

Segmentation Methods ◽

Feed Forward Network ◽

Single Input ◽

Inter Frame

This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network is able to discover objects from a single input image. The rough idea is that an image typically consists of multiple object instances (like the foreground and background) that have spatial transformations across video frames and they can be sparsely represented. By exploring the sparsity representation of a video with a neural network, one may learn the features of each object instance without any labels, which can be used to discover, recognize, or distinguish object instances from a single image. In this paper, we consider a relatively simple scenario, where each image roughly consists of a foreground and a background. Our proposed method is based on encoder-decoder structures to sparsely represent the foreground, background, and segmentation mask, which further reconstruct the original images. We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as the input for inference. The experimental results on various object segmentation benchmarks demonstrate that the proposed method extracts primary objects accurately and robustly, which suggests that unsupervised image learning tasks can benefit from the sparsity of images and the inter-frame structure of videos.

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