Recently, a method for removing shadows from colour images was developed (Finlayson et al. in IEEE Trans. Pattern Anal. Mach. Intell. 28:59---68, 2006) that relies upon finding a special direction in a 2D chromaticity feature space. This "invariant direction" is that for which particular colour features, when projected into 1D, produce a greyscale image which is approximately invariant to intensity and colour of scene illumination. Thus shadows, which are in essence a particular type of lighting, are greatly attenuated. The main approach to finding this special angle is a camera calibration: a colour target is imaged under many different lights, and the direction that best makes colour patch images equal across illuminants is the invariant direction. Here, we take a different approach. In this work, instead of a camera calibration we aim at finding the invariant direction from evidence in the colour image itself. Specifically, we recognize that producing a 1D projection in the correct invariant direction will result in a 1D distribution of pixel values that have smaller entropy than projecting in the wrong direction. The reason is that the correct projection results in a probability distribution spike, for pixels all the same except differing by the lighting that produced their observed RGB values and therefore lying along a line with orientation equal to the invariant direction. Hence we seek that projection which produces a type of intrinsic, independent of lighting reflectance-information only image by minimizing entropy, and from there go on to remove shadows as previously. To be able to develop an effective description of the entropy-minimization task, we go over to the quadratic entropy, rather than Shannon's definition. Replacing the observed pixels with a kernel density probability distribution, the quadratic entropy can be written as a very simple formulation, and can be evaluated using the efficient Fast Gauss Transform. The entropy, written in this embodiment, has the advantage that it is more insensitive to quantization than is the usual definition. The resulting algorithm is quite reliable, and the shadow removal step produces good shadow-free colour image results whenever strong shadow edges are present in the image. In most cases studied, entropy has a strong minimum for the invariant direction, revealing a new property of image formation.

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Entropy Minimization for Shadow Removal

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme

/pdf/a-reversible-data-hiding-scheme-based-on-side-match-vector-2grvhtcunz.pdf

A Reversible Data Hiding Scheme Based on Side Match Vector Quantization

/pdf/a-tutorial-of-the-wavelet-transform-3cdx77os0x.pdf

A Tutorial of the Wavelet Transform

Threshold visual secret sharing by random grids

Visual secret sharing by random grids revisited

This textbook introduces the Fundamentals of Multimedia, addressing real issues commonly faced in the workplace The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website

Fundamentals of multimedia

In this chapter, we discuss the uses of the term “multimedia” since people may have quite different, even opposing, viewpoints on what this means. This textbook is aimed at computer science or engineering students, and consequently a more application-oriented view of what multimedia consists of is what is emphasized. The convergence going on in this field, with computers, smartphones, games, digital TV including 3D, multimedia-based search, and so on converging in technology, means that multimedia is a field that is essentially mandatory for such students to study. Moreover with the pervasive penetration of wireless mobile networks and development of mobile applications for smartphones and tablets, and the advent of social media, the contents of a multimedia course arguably forms the basis for much of the further studies many students will engage in. The components of multimedia are first introduced and then current multimedia research topics and projects are discussed to put the field into a perspective of what is actually at play at the edge of work in this field. For a fuller perspective, the remarkably short history of multimedia is synopsized, from the development of the World Wide Web up to current pervasive social media and anytime/anywhere access. Since multimedia is indeed a practical field, Chapter 1 also supplies an overview of multimedia software tools, such as video editors and digital audio programs, that are typically used to produce multimedia products such as those that are indeed produced in a course in this subject.

Introduction to Multimedia

Recent years have seen an explosion in the availability of digital images. In this chapter, we examine some current image compression standards and demonstrate how techniques presented in Chaps. 7 and 8 are applied in practice. We first describe how transform coding based on DCT (Discrete Cosine Transform), quantization, and entropy coding are explored in the standard JPEG, used in most images, then go on to look at the wavelet-based JPEG2000 standard. Two other standards, JPEG-LS—aimed particularly at a lossless JPEG, outside the main JPEG standard—and JBIG, for bilevel image compression, are included for completeness.

Image Compression Standards

In this chapter, data compression as it relates to multimedia information is studied from the point of view of lossless algorithms, where the input data is essentially exactly recoverable from the compressed data Lossy algorithms, for which this is not the case, are presented in Chapter 8. Here we introduce the fundamentals of information theory and algorithms whose goal is a savings in bitrate given the entropy, especially Huffman Coding and its adaptive version. We then study Dictionary-based Coding (as in Winzip) and go on to a detailed discussion of Arithmetic Coding. Finally, Lossless Image Compression is examined specifically.

Lossless Compression Algorithms

Social media, a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, allow the creation and exchange of user-generated content. With the pervasive penetration of wireless mobile networks, the advanced development of smartphones and tablets, and the massive market of mobile applications, social media contents can now be easily generated and accessed at any time and anywhere. They have substantially changed the way organizations, communities, and individuals communicate.

Jiangchuan Liu

Papers

Fundamentals of multimedia

Introduction to Multimedia

Image Compression Standards

Lossless Compression Algorithms

Social Media Sharing