Residual Hybrid Attention Network (RHAN) can restore images of arbitrary compression quality through flexibly fusing high-frequency features in the spatial and frequency domains based on the input quality factor. Spec...
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Residual Hybrid Attention Network (RHAN) can restore images of arbitrary compression quality through flexibly fusing high-frequency features in the spatial and frequency domains based on the input quality factor. Specifically, to remove the compression artifacts, we propose a hybrid attention block (HAB) to adaptively restore the loss of high-frequency components, which parallelly predicts attention maps along two separate dimensions spatial and frequency spectra. To recover the compressed image flexibly and controllably, we further design a modulation decompression block (MDB), which employs a prior factor to learn a pair of modulation parameters and performs adaptively affine transformation on the obtained high-frequency features, thereby achieving high-quality image restoration at arbitrary compression levels. The quantitative and qualitative experiments on various public data sets show that the RHAN achieves the best performance and optimal visual perceptual quality in the JPEG image restoration with arbitrary compression levels.
In this paper, we propose an iterative MPEG super-resolution method based on adaptive projected subgradient method as stated in Yamada and Ogura (2003). We propose an efficient operator that approximates convex projec...
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In this paper, we propose an iterative MPEG super-resolution method based on adaptive projected subgradient method as stated in Yamada and Ogura (2003). We propose an efficient operator that approximates convex projection onto a set characterizing framewise quantization, whereas a conventional method can only handle a convex projection defined for each DCT coefficient of a frame. By using the operator, the proposed method generates a sequence that converges to a solution of super-resolution problem defined in terms of quantization error of MPEG compression.
Multiview video has gained significant interest in recent years. Generally speaking, Multiview video has more than one views from slightly different angles of the same scene. The huge amount of video data and the hete...
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Multiview video has gained significant interest in recent years. Generally speaking, Multiview video has more than one views from slightly different angles of the same scene. The huge amount of video data and the heterogeneous network condition pose great challenge for the transport of multiview video through internet or broadcast networks. In order to support variety of 3D application scenarios, selective transport of multi view video has to be deployed. This paper proposes a priority based selective transport framework of multiview video over MPEG-2 Transport Stream. The proposed framework achieves selective transport by extracting the related view components from MVC video as well as discarding lowest priority view components without obstructing the decoding process at the receiver client when the transport bandwidth is insufficient. To the best of our knowledge, this paper is the first to investigate a priority-based selective transport framework for delivery MVC video over MPEG-2 transport stream.
Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the `cover' image. This paper proposes new features for JPEG blind steganalysis using a combin...
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Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the `cover' image. This paper proposes new features for JPEG blind steganalysis using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index); the Huffman Bit File Index Resolution (HUBFIRE) algorithm proposed uses these functionals to build the classifier using a multi-class Support Vector Machine (SVM). JPEG images spanning a wide range of resolutions are used to create a `stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, that embeds in the spatial domain, a transform-domain embedding scheme: JPEG Hide-and-Seek and Model Based Steganography which employs an adaptive embedding technique. This work employs a multi-class SVM over the proposed `HUBFIRE' algorithm for statistical steganalysis, which is not yet explored by steganalysts. Experiments conducted prove the model's accuracy over a wide range of payloads and embedding schemes.
In this digital era, lot of information are expressed through images. Various social networking websites, such as Facebook, Twitter, MySpace etc. provides a platform for the users to post up almost any type of picture...
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In this digital era, lot of information are expressed through images. Various social networking websites, such as Facebook, Twitter, MySpace etc. provides a platform for the users to post up almost any type of picture or photo. However, with the advancement in image editing technologies, many users have become victims of digital forgery as their uploaded images were forged for malicious activities. We have come up with a system which detects image forgery based on edge width analysis and center of gravity concepts. An algorithm based on edge detection is also used to identify the fuzzy edges in the forged digital image. The forged object in the image is highlighted by applying Flood fill algorithm. Different types of image forgeries like Image splicing, Copy-Move image forgery etc. can be detected. This method also reveals multiple forgeries in the same image. The proposed system is capable of detecting digital image forgeries in various image formats efficiently. The results we obtained after the analysis of different images shows that the proposed system is 95% efficient.
With the evolution of tools used for image editing in today's world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in f...
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ISBN:
(数字)9781728170169
ISBN:
(纸本)9781728170176
With the evolution of tools used for image editing in today's world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in front of the world is determining whether an image has been tempered or not. A very popular manipulation method for tempering images is Copy-move forgery in which a part of an image is duplicated and pasted in some other location of the same image. Major research has been going on in detection of copy-move forgery. A detailed review and analytical discussion has been presented in this paper along with pros and cons of each of the detection techniques used for copy-move forgery detection.
In this paper, a blind statistical method for watermark detection is developed using a symmetrical normal inverse Gaussian (SNIG) prior for modelling the block-DCT coefficients of images. The proposed detector is moti...
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In this paper, a blind statistical method for watermark detection is developed using a symmetrical normal inverse Gaussian (SNIG) prior for modelling the block-DCT coefficients of images. The proposed detector is motivated by the superior ability of the SNIG prior in capturing the statistics of the DCT coefficients as compared to the conventionally used priors. Analytical expressions are derived for the Bayesian log-likelihood ratio and the corresponding mean and variance under null and alternative hypotheses. Extensive experiments are carried out using standard images to study the performance of the proposed detector and the results show that it performs better than several existing detectors in terms of the associated probabilities of false alarm and detection for watermarks of varying strength.
Zero-watermarking is an effective method to better pro tect the copyright of images. This paper proposes a new ze ro-watermarking algorithm based on Non-uniform Block Tr uncation coding (NUBTC). In the proposed scheme...
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ISBN:
(纸本)9781665466127
Zero-watermarking is an effective method to better pro tect the copyright of images. This paper proposes a new ze ro-watermarking algorithm based on Non-uniform Block Tr uncation coding (NUBTC). In the proposed scheme, the ori ginal image is divided into different rectangular grids accor ding to the non-uniform block-truncation-coding partition, a nd the feature matrix is formed by calculating number of rectangles in each 8*8 sub-area. The obtained feature matri x is further mapped to a binary watermark after Arnold s crambling, and both of them are stored as the zero water mark. Experimental results indicate that the extracted zero-watermark could keep more than 90% similarity to the un attacked zero-watermark under JPEG com pression attack a nd other various noise attacks, thus proving that the propo sed zero-watermark scheme is very robust when sufferring attacks.
This paper parallelizes and characterizes an important computer vision application -Scale Invariant Feature transform (SIFT) both on a Symmetric Multiprocessor (SMP) platform and a large scale Chip Multiprocessor (CMP...
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This paper parallelizes and characterizes an important computer vision application -Scale Invariant Feature transform (SIFT) both on a Symmetric Multiprocessor (SMP) platform and a large scale Chip Multiprocessor (CMP) simulator. SIFT is an approach for extracting distinctive invariant features from images and has been widely applied. In many computer vision problems, a real-time or even super-real-time processing capability of SIFT is required. To meet the computation demand, we optimize and parallelize SIFT to accelerate its execution on multi-core systems. Our study shows that SIFT can achieve a 9.7x ~ llx speedup on a 16 -core SMP system. Furthermore, Single Instruction Multiple Data (SIMD) and cache-conscious optimization bring another 85% performance gain at most. But it is still three times slower than the real-time requirement for High-Definition Television (HDTV) image. Then we study the performance of SIFT on a 64 -core CMP simulator. The results show that for HDTV image, SIFT can achieve an excellent speedup of 52 x and run in real-time finally. Besides the parallelization and optimization work, we also conduct a detailed performance analysis for SIFT on those two platforms. We find that load imbalance significantly limits the scalability and SIFT suffers from intensive burst memory bandwidth requirement on the 16 -core SMP system. However, on the 64 -core CMP simulator the memory pressure is not high due to the shared last-level cache (LLC) which accommodates tremendous read-write sharing in SIFT. Thus it does not affect the scaling performance. In short, understanding the characterization of SIFT can help identify the program bottlenecks and give us further insights into designing better systems.
In past few years, multimedia traffic is growing and Internet have maximum portion of multimedia traffic. This traffic trend is expected to increase due to multimedia applications. Best effort Internet architecture po...
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In past few years, multimedia traffic is growing and Internet have maximum portion of multimedia traffic. This traffic trend is expected to increase due to multimedia applications. Best effort Internet architecture poses design limitations for multimedia traffic. IPTV like applications require higher bandwidth, low packet loss, low delays and jitter effects to transmit high quality video contents. Packet loss due to limited band width and congestion can negatively impact on Quality of Experience(QoE). Packet trimming based innovative scheme is proposed in this article to deal packet loss and improve QoE. Proposed scheme eliminate the chance of packet loss during congestion. Packet trimming scheme better handle congestion which improve video smoothness, interactivity and frame quality. Test-bed implementation and subsequent analyses shows promising improvement in video Quality of Experience over same Quality of Service.
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