Enhancing the error detection capabilities of DCT based codecs using compressed domain dissimilarity metrics

Video compression standards are implemented in wireless data transmission technologies to provide multimedia services efficiently. These compression standards generally utilize the Discrete Cosine Transform (DCT) in conjunction with variable length codes (VLC) in order to achieve the required high compression ratios. While providing the necessary high data rates, this technique has the disadvantage of making the system more susceptible to transmission errors. The standard decoders do not manage to detect a large number of corrupted macroblocks, 40.54% not detected for H.263+, contributing to a significant reduction in the end-to-end video quality as perceived by the end-user. This paper presents three dissimilarity metrics which contain both color and texture information and that can be extracted directly from the compressed DCT coefficients. These metrics can be used to enhance the error-detection capabilities of standard DCT based codecs. Simulation results show that the proposed algorithm increases the error detection rate by 54.06% with a gain in peak signal-to-noise ratio (PSNR) of 3.21 dB. This improvement in performance is superior to other solutions found in literature.peer-reviewe

Evaluation of efficient XML interchange (EXI) for large datasets and as an alternative to binary JSON encodings

Current and emerging Navy information concepts, including network-centric warfare and Navy Tactical Cloud, presume high network throughput and interoperability. The Extensible Markup Language (XML) addresses the latter requirement, but its verbosity is problematic for afloat networks. JavaScript Object Notation (JSON) is an alternative to XML common in web applications and some non-relational databases. Compact, binary encodings exist for both formats. Efficient XML Interchange (EXI) is a standardized, binary encoding of XML. Binary JSON (BSON) and Compact Binary Object Representation (CBOR) are JSON-compatible encodings. This work evaluates EXI compaction against both encodings, and extends evaluations of EXI for datasets up to 4 gigabytes. Generally, a configuration of EXI exists that produces a more compact encoding than BSON or CBOR. Tests show EXI compacts structured, non-multimedia data in Microsoft Office files better than the default format. The Navy needs to immediately consider EXI for use in web, sensor, and office document applications to improve throughput over constrained networks. To maximize EXI benefits, future work needs to evaluate EXI’s parameters, as well as tune XML schema documents, on a case-by-case basis prior to EXI deployment. A suite of test examples and an evaluation framework also need to be developed to support this process.http://archive.org/details/evaluationofeffi1094545196Outstanding ThesisLieutenant, United States NavyApproved for public release; distribution is unlimited

深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Shape representation and coding of visual objets in multimedia applications — An overview

Emerging multimedia applications have created the need for new functionalities in digital communications. Whereas existing compression standards only deal with the audio-visual scene at a frame level, it is now necessary to handle individual objects separately, thus allowing scalable transmission as well as interactive scene recomposition by the receiver. The future MPEG-4 standard aims at providing compression tools addressing these functionalities. Unlike existing frame-based standards, the corresponding coding schemes need to encode shape information explicitly. This paper reviews existing solutions to the problem of shape representation and coding. Region and contour coding techniques are presented and their performance is discussed, considering coding efficiency and rate-distortion control capability, as well as flexibility to application requirements such as progressive transmission, low-delay coding, and error robustnes

You Can Mask More For Extremely Low-Bitrate Image Compression

Learned image compression (LIC) methods have experienced significant progress during recent years. However, these methods are primarily dedicated to optimizing the rate-distortion (R-D) performance at medium and high bitrates (> 0.1 bits per pixel (bpp)), while research on extremely low bitrates is limited. Besides, existing methods fail to explicitly explore the image structure and texture components crucial for image compression, treating them equally alongside uninformative components in networks. This can cause severe perceptual quality degradation, especially under low-bitrate scenarios. In this work, inspired by the success of pre-trained masked autoencoders (MAE) in many downstream tasks, we propose to rethink its mask sampling strategy from structure and texture perspectives for high redundancy reduction and discriminative feature representation, further unleashing the potential of LIC methods. Therefore, we present a dual-adaptive masking approach (DA-Mask) that samples visible patches based on the structure and texture distributions of original images. We combine DA-Mask and pre-trained MAE in masked image modeling (MIM) as an initial compressor that abstracts informative semantic context and texture representations. Such a pipeline can well cooperate with LIC networks to achieve further secondary compression while preserving promising reconstruction quality. Consequently, we propose a simple yet effective masked compression model (MCM), the first framework that unifies MIM and LIC end-to-end for extremely low-bitrate image compression. Extensive experiments have demonstrated that our approach outperforms recent state-of-the-art methods in R-D performance, visual quality, and downstream applications, at very low bitrates. Our code is available at https://github.com/lianqi1008/MCM.git.Comment: Under revie

Color Image Encryption using Chaotic Algorithm and 2D Sin-Cos Henon Map for High Security

In every form of electronic communication, data security must be an absolute top priority. As the prevalence of Internet and other forms of electronic communication continues to expand, so too does the need for visual content. There are numerous options for protecting transmitted data. It's important that the transmission of hidden messages in images remain unnoticed to avoid raising any red flags. In this paper, we propose a new deep learning-based image encryption algorithm for safe image retrieval. The proposed algorithm employs a deep artificial neural network model to extract features via sample training, allowing for more secure image network transmission. The algorithm is incorporated into a deep learning-based image retrieval process with Convolution Neural Networks(CNN), improving the efficiency of retrieval while also guaranteeing the security of ciphertext images. Experiments conducted on five different datasets demonstrate that the proposed algorithm vastly improves retrieval efficiency and strengthens data security. Also hypothesised a 2D Sin-Cos-Henon (2D-SCH)-based encryption algorithm for highly secure colour images. We demonstrate that this algorithm is secure against a variety of attacks and that it can encrypt all three colour channels of an image simultaneously

ONTOLOGICAL AND SOCIO-CULTURAL FOUNDATIONS OF THE INTER-GENERATED DISCOURSE IN THE MODERN INFORMATION SOCIETY

Purpose of the study: The article is devoted to understanding the problems of intergenerational discourse and its transformation in ontological and sociocultural reality. The paper substantiates the need to maintain the mechanism of accumulation and reproduction of the experience of ancestors. It is shown that the violation of the transfer of knowledge and traditions leads to the distortion or disappearance of universal cultural codes. Methodology: In this article, cultural, demographic, and psychological approaches are used to study the ontological and sociocultural foundations of intergenerational discourse. It is necessary to show the influence of historical and socio-cultural transformations on the characteristics of interaction between generations, to determine the form of transfer and assimilation of experience within the family, to demonstrate the socially significant consequences of the demographic revolution in the modern information society. Main Findings: Having outlined only a few reasons for the intergenerational discourse in the field of translation of sociocultural experience, it can be noted that their combination forms the layer of human life in which historically determined values and ideals of human society are realized, methods of accumulation and transfer of experience that are unique for each historical era, new methods communications. Applications of this study: Research results can be applied in the course of social psychology (today, young people are literally imposed a radical cultural gap with previous generations), social philosophy, cultural studies (the form of transfer of experience within the family) and even demography (large-scale changes in human society, with the destruction of human social instincts). Novelty/Originality of this study: As the initial task of the study, it is supposed to identify historical and sociocultural changes in the field of translation and appropriation of experience, to conduct a cultural analysis that gives a clear idea of the evolution of the methods of interaction between generations. An interdisciplinary approach involves a wider coverage of existing concepts and shows that the patterns of development of human society cannot be reduced only to biological, economic or socio-cultural patterns

Combined Source and Channel Strategies for Optimized Video Communications

ISBN 978-953-7619-70-