Peak Transform for Efficient Image Representation and Coding

We investigate the issue of efficient data organization and representation of the curved wavelet coefficients [curved wavelet transform (WT)]. We present an adaptive zero-tree structure that exploits the cross-subband similarity of the curved wavelet transform. In the embedded zero-tree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT), the parent-child relationship is defined in such a way that a parent has four children, restricted to a square of 2×2 pixels, the parent-child relationship in the adaptive zero-tree structure varies according to the curves along which the curved WT is performed. Five child patterns were determined based on different combinations of curve orientation. A new image coder was then developed based on this adaptive zero-tree structure and the set-partitioning technique. Experimental results using synthetic and natural images showed the effectiveness of the proposed adaptive zero-tree structure for encoding of the curved wavelet coefficients. The coding gain of the proposed coder can be up to 1.2 dB in terms of peak SNR (PSNR) compared to the SPIHT coder. Subjective evaluation shows that the proposed coder preserves lines and edges better than the SPIHT coder.

Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compression models need large and diverse datasets to generalize well across different types of video content. Wavelets can provide both time (spatial) and frequency localization, making them highly effective for video compression. This dual localization allows wavelet transforms to handle both rapid changes in video content and slow-moving scenes efficiently, leading to better compression ratios. Yet, some wavelet coefficients may be more critical for maintaining visual quality than others. Inaccurate quantization can lead to noticeable degradation. For the first time, the suggested model combine Quantum Wavelet Transform (QWT) and Neural Networks (NN) for video compression. This fusion model aims to achieve higher compression ratios, maintain video quality, and reduce computational complexity by utilizing QWT’s efficient data representation and NN’s powerful pattern recognition and predictive capabilities. Quantum bits (qubits) can encode large amounts of information in their quantum states, enabling more efficient data representation. This is especially useful for encoding large video files. Furthermore, quantum entanglement allows for correlated data representation across qubits, which can be exploited to capture intricate details and redundancies in video data more effectively than classical methods. The experimental results reveal that QWT achieves a compression ratio of almost twice that of traditional WT for the same video, maintaining superior visual quality due to more efficient redundancy elimination.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

The growing demand for visual telecommunication services over the last decade has greatly increased the need for efficient image coding and compression schemes. The work presented in this thesis examines several aspects of the problem of coding and compressing the various image and video-based services which are likely to utilize Broadband Integrated Services Digital Networks (BISDNs) in the future. This research has two major thrusts. The first is the development of a general-purpose, high-performance and high-quality image coding and compression scheme for these broadband-based visual services. The second is the development of an accurate traffic source modelling scheme for the Variable BitRate (VBR) packet video traffic produced by the proposed coding scheme. The proposed high-performance visual coding and compression scheme combines both statistical and psychovisual coding techniques to produce an optimum scheme which removes both statistical and psychovisual redundancies from images in the coding process. When used to encode four standard 512x512x8-bit test images, this scheme results in sub-distortion compression ratios of up to 27:1 without the use of any form of interframe coding. An efficient and flexible Asynchronous Transfer Mode (ATM) cell packaging scheme is also developed which allows the production of either 'priority' or 'non-priority' cell streams suitable for transmission over broadband networks. Three suitable VBR packet video traffic source models are developed during this study. Each of these sources reproduces the 'low-level' cell generation process and switches between different 'modes' of cell generation in order to capture the inherent `burstiness' and 'variability' of typical VBR packet video traffic streams. However, the way in which this cell production process is modelled, as well as the traffic levels which can be reproduced, differs for each of these sources. A relatively complex, general-purpose traffic source is proposed which is based on the hidden Markov statistical model and is able to model all levels of VBR traffic (up to 20 Mbps). A simpler 'switched-fractal' source is also proposed as an accurate model for the low (below 5 Mbps) VBR traffic only. The third proposed source is a 'switched-Markov' model which specifically caters for the high (5 to 20 Mbps) levels of VBR traffic. Among these three artificial traffic sources, the characteristics of all levels of traffic produced by the proposed video and image coding scheme are able to be reproduced accurately.

Image and video coding plays a salient role in numerous multimedia applications. Nowadays, digital video communication is adopted in many applications, for example broadcast services over satellite and terrestrial channels, digital video storage, and more. It is impractical for us to store the full digital video without processing. It is well-known that video compression is a process of diminishing the size of data used to depict digital video images, and it is an amalgamation of spatial image compression and temporal motion compensation. The primary intent of our research is to propose a very potent video compression system. Originally, wavelet decomposition is applied to the I-frame and the resulting coefficients are quantized with the aid of Listless SPECK (LSK) algorithm. Secondly, an adaptive rood search with spatio-temporal correlation technique (ARS-ST) is employed for motion estimation and it finds the displacement of the blocks of P-frame from the I-frame. The proposed video compression technique is evaluated by using different videos and the compression efficiency is computed by determining the peak signal to noise ratio (PSNR) values. Also, the efficacy of the proposed system is compared with existent video compression systems. From the comparative study, it can be seen that our proposed system robustly compress the videos with adequate PSNR measurements.

Recent studies have revealed that human and non-human animals (rats) can detect luminance distribution and correlations between pixels in an image (ranging from 2-point to 4-point). This sensitivity is believed to stem from optimization processes in the visual system operating through efficient coding mechanisms that retain the most informative and significant features (here identified as the most variable correlations), thereby reducing costs to extract biologically relevant information from the environment. However, it is yet to be determined whether this optimization is evolutionarily given by inborn mechanisms or shaped by visual experience. Here we report that newly hatched visually naive domestic chicks (Galluls gallus) spontaneously prefer to approach luminance, 2-point and 4-point correlation patterns (respectively, horizontal lines and rectangular patterns), while showing no preference for 3-point correlation over noise controls. This parallels the ranking observed in adult humans and rats, thus suggesting that evolutionarily given biological predispositions largely drive efficient coding of natural images. We also found that learning by exposure to visual stimuli, as occurs naturally during visual imprinting, induced a preference for noise over point correlation patterns in chicks exposed to 3- and 4-point patterns. We hypothesize that this behaviour could reflect chicks' preference for stimuli of lower statistical (correlation-based) complexity.

Common Lisp [25],[26] includes a dynamic datatype system of moderate complexity, as well as predicates for checking the types of language objects. Additionally, an interesting predicate of two “type specifiers”—SUBTYPEP—is included in the language. Thissubtypep predicate provides a mechanism with which to query the Common Lisp type system regarding containment relations among the various built-in and user-defined types. Whilesubtypep is rarely needed by an applications programmer, the efficiency of a Common Lisp implementation can depend critically upon the quality of itssubtypep predicate: the run-time system typically calls uponsubtypep to decide what sort of representations to use when making arrays; the compiler calls uponsubtypep to interpret userdeclarations, on which efficient data representation and code generation decisions are based. As might be expected due to the complexity of the Common Lisp type system, there may be type containment questions which cannot be decided. In these casessubtypep is expected to return “can't determine”, in order to avoid giving an incorrect answer. Unfortunately, most Common Lisp implementations have abused this license by answering “can't determine” in all but the most trivial cases.In particular, most Common Lisp implementations of SUBTYPEP fail on the basic axioms of the Common Lisp type system itself [25][26]. This situation is particularly embarrassing for Lisp-the premier “symbol processing language”—in which the implementation of complex symbolic logical operations should be relatively easy. Sincesubtypep was presumably included in Common Lisp to answer thehard cases of type containment, this “lazy evaluation” limits the usefulness of an important language feature. This paper shows how those type containment relations of Common Lisp which can be decided at all, can be decided simply and quickly by a decision procedure which can dramatically reduce the number of occurrences of the “can't determine” answer fromsubtypep. This decision procedure doesnot require the conversion of a type specifier expression to conjunctive or disjunctive normal form, and therefore does not incur the exponential explosion in space and time that such a conversion would entail. The lattice mechanism described here for decidingsubtypep is also ideal for performingtype inference [2]; the particular implementation developed here, however, is specific to the type system of Common Lisp [4]. Categories and Subject Descriptors: Lisp, dynamic typing, compiler optimization, type inference, decision procedure.

Digital medical images are becoming even more widespread and used in a large variety of applications. Such images are often stored in local repositories and need to be transmitted/ received over the network. Therefore, data compression is an essential mechanism to adopt for improving the transmission time, as well as to optimize the required storage space. However, due to the clinical relevance of such data, lossless techniques are almost ever preferred, given that in such context it is not tolerated any loss of data. In this work, starting from the Medical Images Lossless Compression (MILC) algorithm, which enables the efficient coding of three-dimensional medical images achieving results comparable with the other state-of-art algorithms, we propose Parallel MILC, a fully parallelized version of that algorithm, which provides some attractive features with respect to MILC, especially in terms of execution speedup and scalability. In detail, we propose a novel design and implementation for the MILC algorithm, which is able to exploit the power and the capabilities of the parallel computing paradigm. It is important to point out that Parallel MILC can be executed on several heterogeneous device types supporting OpenCL (i.e. CPU, GPU, FPGA, etc.). The preliminary test results show a significant performance speedup of Parallel MILC compared to MILC. Consequently, the novel algorithm we propose provides MILC with strong scalability properties and complete transparency with respect the underlying hardware.

The information is getting precious day by day because of the digital revolution and more reach exposure of citizens to technology. Information security is in demand and the topmost priority among researchers to develop data security systems. Image steganography is an invisible data hiding scheme, although it uses visual image media to hide information. Steganography is not only limited to images. There are other media like text, audio, or video that can hide information. In this research work, a novel data hiding is being developed using an image as cover media. During the development of the technique, researchers face maintaining the payload capacity and higher Peak Signal to Noise Ratio (PSNR) values while maintaining other parameters. This work uses color images as cover and digital bit modification methods to hide secure information. Only hiding is not enough to secure information. An additional layer of security is added by encrypting a secure message using an encryption method. The digital bit modification method is unconventionally spread the sequential bitstream over channels. This would make this approach unique and better as it keeps the PSNR level higher along with NCC and lowers MSE. All this is done by keeping the payload capacity as the maximum available. According to the proposed algorithm results, the average value of PSNR is 60.295dB, NCC is 1.00, and MSE is 0.1567.

Purpose–This study aims to improve the embedding process and the integration of an additional layer of security to enhance the overall security of the based Least Significant Bit Algorithm. Method–The study incorporated the use of NTRUEncryptfor the encryption of the plaintext message, and a randomized embedding technique by generating random pixel locations based on the Lorenz Chaos System.This wasassessed using histogramanalysis to showthedifference between the original and the stego-image, MeanSquaredError(MSE), and Peak signal-to-noise ratio(PSNR). Results–The modification has been evaluated using a histogram along with the result of its PSNR and MSE, at which the modified least significant bit algorithm obtains the highest PSNR score of 77.078%and the lowest MSE score of 0.0012%forthe 512x512 image size,which resulted in a less distorted image and better quality of the image compared to the original base LSB. Conclusion–The modification of the least significant bit algorithm successfully implemented a randomized embedding process through the Lorenz System and encrypted the secret message through NTRUEncrypt before embedding to further secure the message inside the image. Image distortion was also lessened as tested from the different sizes of images, which were testedthrough the PSNR and MSE metrics. Recommendations–The study suggests the implication of machine learning algorithms in generating key pixel locations for data embedding and the use of text compression algorithms to reduce the size of the embeddedsecret message. Research Implications–This study is a significant resource for future researchersinterested in exploring literature on image steganography and secure communication.Thus, it introduces a novel approach by integrating lattice-based encryption and chaos systems, thereby enhancing secure communication through digital image steganography. Keywords–digital image signature,image steganography, least significant bit, Lorenz system, NTRUEncrypt

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.

Embedded zero tree wavelet coding (EZW) is an effective image coding algorithm, Study on the output stream of P, N, T, Z symbol, they are appeared with inequality probability. In order to reduce the desired number of digits used in coding, this paper proposes an EZW and Huffman joint encoding algorithm. The experimental results shows, compared with the independent EZW algorithm, the joint encoding algorithm can improve the efficiency of image compression and coding.

JPEG image coding standard has been a dominant format in a wide range of applications in soon three decades since it has been released as an international standard. The landscape of applications and services based on pictures has evolved since the original JPEG image coding algorithm was designed. As a result of progress in the state of the art image coding, several alternative image coding approaches have been proposed to replace the legacy JPEG with mixed results. The majority among them have not been able to displace the unique position that the legacy JPEG currently occupies as the ubiquitous image coding format of choice. Those that have been successful, have been so in specific niche applications. In this paper, we analyze why all attempts to replace JPEG have been limited so far, and discuss additional features other than compression efficiency that need to be present in any modern image coding algorithm to increase its chances of success. Doing so, we compare rate distortion performance of state of the art conventional and learning based image coding, in addition to other desired features in advanced multimedia applications such as transcoding. We conclude the paper by highlighting the remaining challenges that need to be overcome in order to design a successful future generation image coding algorithm.

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).

In this paper, we present a new image steganography technique based on Integer Wavelet Transform (IWT) and Munkres' assignment algorithm which embeds secret image in frequency domain of cover image with high matching quality. IWT is used to transform both cover and secret images from spatial domain to frequency domain, and assignment algorithm is used for best matching between blocks for embedding. We embed the secret image in different coefficients of cover image bands such as horizontal detail, vertical detail and diagonal detail and observe the effect of embedding on the performance of stego image in terms of Peak Signal to Noise Ratio (PSNR). Experimental results depict that stego image and extracted secret image could have high visual quality and they are perceptually similar to their original versions. In addition, this method shows high robustness against six different attacks. Embedding in diagonal detail coefficients gives better PSNR than other coefficients.