Edge-guided image gap interpolation using multi-scale transformation

This paper presents improvements in image gap restoration through the incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed: 1) the local edges or textures, inferred from the gradients of the neighboring pixels and 2) the global edges between image objects or segments, inferred using a Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer, an edge skeleton image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of upsampling), up-sampled, and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves peak-signal-to-noise-ratio by 1–5 dB compared with a range of best-published works

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Frame Interpolation for Cloud-Based Mobile Video Streaming

© 2016 IEEE. Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds

An investigation into the requirements for an efficient image transmission system over an ATM network

This thesis looks into the problems arising in an image transmission system when transmitting over an A TM network. Two main areas were investigated: (i) an alternative coding technique to reduce the bit rate required; and (ii) concealment of errors due to cell loss, with emphasis on processing in the transform domain of DCT-based images. [Continues.

Robust and efficient video/image transmission

The Internet has become a primary medium for information transmission. The unreliability of channel conditions, limited channel bandwidth and explosive growth of information transmission requests, however, hinder its further development. Hence, research on robust and efficient delivery of video/image content is demanding nowadays. Three aspects of this task, error burst correction, efficient rate allocation and random error protection are investigated in this dissertation. A novel technique, called successive packing, is proposed for combating multi-dimensional (M-D) bursts of errors. A new concept of basis interleaving array is introduced. By combining different basis arrays, effective M-D interleaving can be realized. It has been shown that this algorithm can be implemented only once and yet optimal for a set of error bursts having different sizes for a given two-dimensional (2-D) array. To adapt to variable channel conditions, a novel rate allocation technique is proposed for FineGranular Scalability (FGS) coded video, in which real data based rate-distortion modeling is developed, constant quality constraint is adopted and sliding window approach is proposed to adapt to the variable channel conditions. By using the proposed technique, constant quality is realized among frames by solving a set of linear functions. Thus, significant computational simplification is achieved compared with the state-of-the-art techniques. The reduction of the overall distortion is obtained at the same time. To combat the random error during the transmission, an unequal error protection (UEP) method and a robust error-concealment strategy are proposed for scalable coded video bitstreams

Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communication (e.g., telepresence, live streaming, and internet video) and multimedia forensics. To address this, we introduce the bitstream-corrupted video (BSCV) benchmark, the first benchmark dataset with more than 28,000 video clips, which can be used for bitstream-corrupted video recovery in the real world. The BSCV is a collection of 1) a proposed three-parameter corruption model for video bitstream, 2) a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and 3) a plug-and-play module in video recovery framework that serves as a benchmark. We evaluate state-of-the-art video inpainting methods on the BSCV dataset, demonstrating existing approaches' limitations and our framework's advantages in solving the bitstream-corrupted video recovery problem. The benchmark and dataset are released at https://github.com/LIUTIGHE/BSCV-Dataset.Comment: Accepted by NeurIPS Dataset and Benchmark Track 202