Visual Redundancy Research Articles

MGraph: multimodal event summarization in social media using topic models and graph-based ranking

Due to the increasing popularity of social media platforms, the amount of messages (posts) related to public events, especially posts sharing multimedia content, is steadily increasing. Sharing images can contribute to a rich and live coverage of the event. Yet, despite the value and interestingness of some posts, there is a lot of spam and redundancy, which makes it challenging to select the most important and characteristic posts for the event. In this work, we describe MGraph, a summarization framework that, given a set of social media posts about an event, selects a subset of shared images, simultaneously maximizing their relevance and minimizing their visual redundancy. MGraph employs a topic modelling technique based on different modalities to capture the relevance of posts to event topics, and a graph-based ranking algorithm to produce a diverse ranking of the selected high-relevance images. A user-centred evaluation on a dataset comprising a variety of real-world events demonstrates that MGraph considerably outperforms a number of state-of-the-art summarization algorithms in terms of relevance and diversity (25 and 7 % improvement respectively).

Video Summarization Based on a Fuzzy Based Incremental Clustering

The significant development of multimedia and dijital video production in recent years has led to the mass production of personal and commerical video archives.Therefore, the need for efficient tools and methods of accessing video content and information rapidly is significantly increasing. Video summarization is the removal of visual redundancy and repetitive video frames,and obtaining a short summary of the whole video so that the summary obtained effectively reflects the whole video content. Examples of these summarizations in recent years include STIMO and VSUMM.According to users' comments, in the mentioned methods, the summarization has a high rate of error in a full report of summarization and a low accuracy in non-repetitive frames production, as well as a high computation time.In this paper.in order to solve these problems,we developed a system which modeled users' and supervisors' comments.We used a fuzzy based incremental clustering by which the selection and deselection of frames are done based on fuzzy rules. The extracted rules were determined based on users' comments on the video summarization.Finally, we performed our proposed method on the video clips used in the previous methodes.Produced summaries were evaluated by a qualitative method to minimize human interferences.The results obtained indicate the high accuracy of summarization and the less computation time. DOI: http://dx.doi.org/10.11591/ijece.v4i4.5836

Redundant Use of Visual Images of Different Domain Information Steganographic Technique

The full realization of human visual redundancy safely embedded secret information is information hiding goal Steganography existing image information technology in many fields of image data to achieve the same paper will quantify the signal compression coding theory and methods. information used in dense cover, we propose a fully redundant human visual images of different domain information steganographic method and designed three new error correction method to ensure the accuracy of blind ciphertext information extraction and recovery experiments show that the algorithm spatial statistical properties of the image data changes less with a capacity of more hidden PCT Gao and safety.

An Automated Threshold Selection using Wavelet Based PSO for Image Compression

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies known as coding redundancy, Inter pixel redundancy and psycho visual redundancy. Recent research in transformbased image compression has focused on the wavelet transform due to its superior performance over other transforms. In the recent survey, image is subjected to wavelet decomposition to obtain wavelet coefficients and then applied Hard or Soft thresholds for neglecting certain wavelet coefficients by manually selecting global and local threshold values. Here, the manual threshold selection becomes very difficult because it depends on the type of image and its statistical properties like mean and standard deviation. In this paper we proposed an automated threshold selection scheme using wavelet based Particle Swarm Optimization which maintains trade of between peak signal to noise ratio (PSNR) and compression ratio (CR). The above work is implemented using MATLAB 2009.

Multilevel region of interest guided bit allocation for multiview video coding

Multiview video coding (MVC) is an efficient compression scheme, by which the large amount of multiview video data can be effectively processed. However, it ignores the characteristics of the human visual system. In this paper, we propose a multilevel region of interest (ROI) based bit allocation strategy for MVC, which can take advantage of the visual redundancy to improve encoding efficiency. First, the macro block (MB) saliency is derived from depth information of the video sequence, and the multilevel ROI segmentation is conducted based on the MB saliency distribution. Then, the multiview video bit allocation strategy based on multilevel ROI is proposed. We have evaluated system performance with several multiview video sequences in JMVC 8.5 reference software. Experimental results show that the quality of ROIs has a considerable improvement when the bit rate consumed is kept consistent with JMVC. Meanwhile, the proposed MVC method can save bit rate while maintaining the overall image quality.

RELIABLE DATA TRANSMISSION OVER WIRELESS NETWORK USING IMAGE STEGANOGRAPHY

Image steganography is the science of hiding data inside cover images for security. Images have a lot of visual redundancy in the sense that our eyes do not usually care about subtle changes in color in an image region. One can use this redundancy to hide text, audio or even image data inside cover images without making significant changes to the visual percept ion. Image steganography is becoming popular on the internet these days since a steganography image, which just looks like any other image, attracts a lot less attention than an encrypted text and a secure channel. Steganography is the science of hiding messages in such a way that no one apart from the sender and the intended recipient, suspects the existence of the message. There are multiple techniques in order to embed data in an image, however some techniques are better at being undetected then others. These techniques depend on three different aspects: capacity, security, and robustness. Capacity refers to the amount of information that can be hidden in the cover medium. Security to an eavesdropper‟s inability to detect hidden information. Robustness to the amount of modification the stego medium can withstand before an adversary can destroy hidden information.

Temporal characteristics of the effects of visual pattern redundancy on encoding and storage processes: evidence from rapid serial visual presentation

Pattern redundancy is a key concept for representing the amount of internal mental load (encoding efficiency) needed for pattern perception/recognition. The present study investigated how pattern redundancy influences encoding and memory processes in the visual system using a rapid serial visual presentation (RSVP) paradigm. With RSVP, it is well known that participants often fail to detect repetitions of words (repetition blindness, RB). We used this phenomenon as an index of the encoding and storage of visual patterns. In three experiments, we presented patterns with higher and lower redundancy, as defined by Garner's equivalent set size (ESS). The results showed that RB occurred more frequently for higher redundancy patterns when the temporal distance between the targets was less than 500 ms; this tendency was reversed with longer temporal distances of over 500 ms. Our results suggest that pattern redundancy modulates both the early encoding and subsequent memory processes of a representation.

Is one enough? The case for non-additive influences of visual features on crossmodal Stroop interference

When different perceptual signals arising from the same physical entity are integrated, they form a more reliable sensory estimate. When such repetitive sensory signals are pitted against other competing stimuli, such as in a Stroop Task, this redundancy may lead to stronger processing that biases behavior toward reporting the redundant stimuli. This bias would therefore, be expected to evoke greater incongruency effects than if these stimuli did not contain redundant sensory features. In the present paper we report that this is not the case for a set of three crossmodal, auditory-visual Stroop tasks. In these tasks participants attended to, and reported, either the visual or the auditory stimulus (in separate blocks) while ignoring the other, unattended modality. The visual component of these stimuli could be purely semantic (words), purely perceptual (colors), or the combination of both. Based on previous work showing enhanced crossmodal integration and visual search gains for redundantly coded stimuli, we had expected that relative to the single features, redundant visual features would have induced both greater visual distracter incongruency effects for attended auditory targets, and been less influenced by auditory distracters for attended visual targets. Overall, reaction times were faster for visual targets and were dominated by behavioral facilitation for the cross-modal interactions (relative to interference), but showed surprisingly little influence of visual feature redundancy. Post-hoc analyses revealed modest and trending evidence for possible increases in behavioral interference for redundant visual distracters on auditory targets, however, these effects were substantially smaller than anticipated and were not accompanied by a redundancy effect for behavioral facilitation or for attended visual targets.

Text Watermarking Algorithm based on Human Visual Redundancy

Text digital watermarking, which is an important research direction of information hiding, can be used for copyright protection of text document. This paper proposed a text digital watermarking algorithm based on human visual redundancy. As the human eye is not sensitive to the slight change for text color, watermarks were embedded by changing the low-4 of RGB color components of characters. Experiment shows that the proposed method has good invisibility and robustness to resist deletion, modification attack etc. As long as there are N/8.6 consecutive characters left in the document, the watermarks can still be extracted completely using proposed algorithm.

The effect of visual redundancy on repetition blindness and its temporal properties

The effect of visual redundancy on repetition blindness and its temporal properties

Stereoscopic Visual Attention-Based Regional Bit Allocation Optimization for Multiview Video Coding

We propose a Stereoscopic Visual Attention- (SVA-) based regional bit allocation optimization for Multiview Video Coding (MVC) by the exploiting visual redundancies from human perceptions. We propose a novel SVA model, where multiple perceptual stimuli including depth, motion, intensity, color, and orientation contrast are utilized, to simulate the visual attention mechanisms of human visual system with stereoscopic perception. Then, a semantic region-of-interest (ROI) is extracted based on the saliency maps of SVA. Both objective and subjective evaluations of extracted ROIs indicated that the proposed SVA model based on ROI extraction scheme outperforms the schemes only using spatial or/and temporal visual attention clues. Finally, by using the extracted SVA-based ROIs, a regional bit allocation optimization scheme is presented to allocate more bits on SVA-based ROIs for high image quality and fewer bits on background regions for efficient compression purpose. Experimental results on MVC show that the proposed regional bit allocation algorithm can achieve over % bit-rate saving while maintaining the subjective image quality. Meanwhile, the image quality of ROIs is improved by dB at the cost of insensitive image quality degradation of the background image.

Low-bit-rate coding of underwater color image using improved wavelet difference reduction

Underwater image compression has been the key technology for transmitting massive amount of image data via underwater acoustic channel with limited bandwidth. According to the characteristics of underwater color images, an efficient underwater image compression method has been developed. The new coding scheme employs a wavelet-based preprocessing method to remove the visual redundancy, and adopts a Wavelet Tree-based Wavelet Difference Reduction (WTWDR) algorithm to remove the spatial redundancy of underwater color images. Instead of scanning whole transformed image like the WDR method, the difference reduction coding is used for each significant wavelet tree in the proposed WTWDR algorithm based on the correlation between the subbands of higher levels and lower levels of a transformed image. The experimental results show that for underwater color images the proposed method outperforms both WDR and SPIHT at very low bit rates in terms of compression ratio and reconstructed quality, while for natural images it has similar performance with WDR and SPIHT. Hence, the proposed approach is especially suitable for underwater color image compression at very low bit rates.

Block-Based Image Compression With Parameter-Assistant Inpainting

This correspondence presents an image compression approach that integrates our proposed parameter-assistant inpainting (PAI) to exploit visual redundancy in color images. In this scheme, we study different distributions of image regions and represent them with a model class. Based on that, an input image at the encoder side is divided into featured and non-featured regions at block level. The featured blocks fitting the predefined model class are coded by a few parameters, whereas the non-featured blocks are coded traditionally. At the decoder side, the featured regions are restored through PAI relying on both delivered parameters and surrounding information. Experimental results show that our method outperforms JPEG in featured regions by an average bit-rate saving of 76% at similar perceptual quality levels.

Compresión de imágenes: Fundamentos, técnicas y formatos

The work that is presented in this article is developed in the digital Processing of images area, specifically in the image compression for a brief description of the principal technologies and more used standard formats of image codification. The compression of images includes a set of techniques that are applied to the images to store them or to transmit them in an efficient way. Three forms are identified at least to reduce the number of redundant information in an image: to eliminate redundant code, to eliminate redundant pixels and to eliminate visual redundancy. In general the techniques of image compression can belong to two big classes: a) Without loss of information, lossless, and b) With loss of information, lossy. In lossless, is not permissible loss of information in the process of compression, for example in medical or legal images. Whereas, in other images it is possible to allow certain degree of loss, though supporting the quality of the image, with the purpose of optimizing the compression of images, for example in images of video conferences. Finally, there are hundreds of image formats, in this work more acquaintances are described.

A perceptual approach to trimming and tuning unstructured lumigraphs

We present a novel perceptual method to reduce the visual redundancy of unstructured lumigraphs, an image based representation designed for interactive rendering. We combine features of the unstructured lumigraph algorithm and image fidelity metrics to efficiently rank the perceptual impact of the removal of subregions of input views ( subviews ). We use a greedy approach to estimate the order in which subviews should be pruned to minimize perceptual degradation at each step. Renderings using varying numbers of subviews can then be easily visualized with confidence that the retained subviews are well chosen, thus facilitating the choice of how many to retain. The regions of the input views that are left are repacked into a texture atlas. Our method takes advantage of any scene geometry information available but only requires a very coarse approximation. We perform a user study to validate its behaviour, as well as investigate the impact of the choice of image fidelity metric as well as that of user parameters. The three metrics considered fall in the physical, statistical and perceptual categories. The overall benefit of our method is the semiautomation of the view selection process, resulting in unstructured lumigraphs that are thriftier in texture memory use and faster to render. Using the same framework, we adjust the parameters of the unstructured lumigraph algorithm to optimise it on a scene by scene basis.

Image Compression With Edge-Based Inpainting

In this paper, image compression utilizing visual redundancy is investigated. Inspired by recent advancements in image inpainting techniques, we propose an image compression framework towards visual quality rather than pixel-wise fidelity. In this framework, an original image is analyzed at the encoder side so that portions of the image are intentionally and automatically skipped. Instead, some information is extracted from these skipped regions and delivered to the decoder as assistant information in the compressed fashion. The delivered assistant information plays a key role in the proposed framework because it guides image inpainting to accurately restore these regions at the decoder side. Moreover, to fully take advantage of the assistant information, a compression-oriented edge-based inpainting algorithm is proposed for image restoration, integrating pixel-wise structure propagation and patch-wise texture synthesis. We also construct a practical system to verify the effectiveness of the compression approach in which edge map serves as assistant information and the edge extraction and region removal approaches are developed accordingly. Evaluations have been made in comparison with baseline JPEG and standard MPEG-4 AVC/H.264 intra-picture coding. Experimental results show that our system achieves up to 44% and 33% bits-savings, respectively, at similar visual quality levels. Our proposed framework is a promising exploration towards future image and video compression.

Psychosocial Development of Children with Hearing Loss

Psychosocial Development of Children with Hearing Loss

Effects of Visual Intensity and Audiovisual Redundancy in Bad News

This experiment investigated the effects of visual intensity in audiovisual redundancy research. Results indicate that viewers' recognition and delayed free recall of compelling messages were unimpeded by redundancy. For noncomplying messages, however, the 2 cognitive indicators both suffered when redundancy was absent. Results of this study suggest that redundancy did not have a blanket effect. The variance explained by visual intensity suggests that human processing of audio and visual information could be better understood by taking into consideration cognitive variables such as redundancy, as well as affective variables such as visual intensity.

Exploring video content structure for hierarchical summarization

In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot- segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units. A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.

Video summarization and retrieval using singular value decomposition

In this paper, we propose novel video summarization and retrieval systems based on unique properties from singular value decomposition (SVD). Through mathematical analysis, we derive the SVD properties that capture both the temporal and spatial characteristics of the input video in the singular vector space. Using these SVD properties, we are able to summarize a video by outputting a motion video summary with the user-specified length. The motion video summary aims to eliminate visual redundancies while assigning equal show time to equal amounts of visual content for the original video program. On the other hand, the same SVD properties can also be used to categorize and retrieve video shots based on their temporal and spatial characteristics. As an extended application of the derived SVD properties, we propose a system that is able to retrieve video shots according to their degrees of visual changes, color distribution uniformities, and visual similarities.