Phase component compression for computer-generated holograms using modified JPEG-LS prediction

For many years, lossless image compression has been a promising topic of study. Various techniques have been created over time to obtain an approximation of the reduced data size. While discrete wavelet transform (DWT) and discrete cosine transform (DCT) have historically been employed for the purpose of compressing images, various machine learning methods and deep learning networks are now being offered. In this research, we conduct a comparative analysis of conventional and contemporary lossy image compression techniques on the Kodak Dataset, including Autoencoders, Principal Component Analysis (PCA), K-Means, and Discrete Wavelet Transform (DWT). The metrics used for the evaluation of the proposed study are Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), and Structural Similarity Index (SSIM).

Recent advances in computer-generated holography (CGH) have significantly improved visual quality through high-resolution rendering; however, the accompanying increase in data size has become a critical obstacle to practical deployment. Conventional image compression techniques such as JPEG and high efficiency video coding (HEVC) do not adequately account for the unique statistical and spectral characteristics of holograms, thereby limiting both compression efficiency and reconstruction quality. This study proposes an efficient compression framework specialized for digital holograms by integrating a ringing reduction technique for diffraction calculations with CompressAI, a deep learning-based image compression framework. We demonstrate that ringing artifacts are a major factor hindering efficient hologram compression, and show that neural networks can achieve superior performance by learning the intrinsic statistical and spectral features of holograms. Our method achieves a superior balance between compression efficiency and reconstruction quality compared to conventional approaches, particularly at low bit rates. Furthermore, by introducing a multi-channel input representation, our method achieves higher compression ratios.

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerate compression process. In this paper, we use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing this compression method it is applied on different images from Corel dataset for experimental results and analysis. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

The past few years have seen an increasing interest in using reversible integer wavelets in image compression. Reversible integer wavelet image coders facilitate decompression from low bit rates all the way up to lossless reconstruction. However, in the past, specific implementations of such techniques, like S+P, could not match the lossless compression performance of state-of-the-art predictive coding techniques like CALIC. We demonstrate for the first time that reversible integer wavelets together with proper context modeling can match the lossless compression performance of CALIC. This can be done without increase in the essential complexity over S+P. Our findings present a strong argument for using subband coding as a unified, elegant approach for both lossy and lossless image compression. Specifically, in this paper we outline how to obtain significantly higher coding efficiency over S+P by utilizing better filters and better context modeling and entropy coding of wavelet coefficients.

Problem statement: Existing lossless image compression schemes attempt to do prediction in an image data using their Local Binary Pattern ( LBP) and their spatial neighborhood based techniques. In the previous techniques such as Vec tor Quantization (VQ) and Gradient Adjusted Prediction (GAP) the texture and non-texture region s are not classified separately. Texture and Non- texture images prophecy has been a key factor in ef ficient lossless image compression. Hence, there is a need to develop a more efficient image prediction scheme to exploit these texture components. Approach: In this research, an efficient visual quality tech nique for image compression is proposed. The image is classified into texture and non-textur e regions by using an Artificial Neural Network (ANN) Classifier. The texture region is encoded wit h the Similar Block Matching (SBM) encoder and the non-texture region is encoded with SPIHT encoding. Results: The proposed texture prediction based compression is compared with the existing compression techniques such as H.264 and JPEG. From the result it reveals that the Peak Signal to Noise Ratio (PSNR) values of all the test images is higher in the proposed technique as compared to JPE G technique. Similarly PSNR values are low in H.264 for all the images except Boat image when compared to the proposed technique. This result concludes that the increase in PSNR indicates that the output image has less noise as compared to existing techniques. Conclusion: The compression of the proposed algorithm is superi or to JPEG and H.264. Our new method of compression algorithm can be used to improve the performance of Compression ratio and Peak Signal to Noise Ratio (P SNR). In future this study can be extended to real time applications for video compression in medical images.

Lossless gray scale image compression is necessary for many purposes, such as medical imaging, image databases and so on. Lossy images are important as well, because of the high compression ratio. The authors propose a lossless image compression scheme using a lossy image generated with the JPEG-DCT scheme. The concept is, send a JPEG-compressed lossy image primarily, then send residual information and reconstruct the original image using both the lossy image and residual information. 3D adaptive prediction and adaptive arithmetic coding are used, which fully use the statistical parameters of the distribution of the symbol source. The optimal number of neighbor pixels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the original lossless algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Lossless image compression via predictive coding of discrete Radon projections

본 논문에서는 기존의 영상/비디오 압축 기술을 홀로그램의 특성을 반영하여 변형한 압축 기술을 제안한다. 본 논문에서는 컴퓨터 생성 홀로그램 기법(computer-generated hologram, CGH)을 이용하여 디지털 홀로그램을 획득한다. 제안한 기술은 디지털 홀로그램의 전처리 기술, CGH로 생성한 홀로그램의 공간영역 분할, 2D-DCT를 이용한 주파수 변환, 주파수 영역에서의 움직임 예측과 차영상 생성 등이다. 이 데이터들을 H.264/AVC 코덱, BinHex과 같은 무손실 부호화 기술, 자체 제작한 선형양자화기를 이용하여 압축한다. 실험결과 10:1의 압축률에서 25.4 dB, 100:1에서 16.5 dB의 복원결과를 보였다. This paper proposes a hologram data compression scheme that uses the existing image/video compression techniques, in which the existing techniques are modified appropriately to fit to the characteristics of hologram. In this paper we use CGH as the hologram data. The proposed scheme uses the generation characteristics of a CGH to consist of a pre-processing, spatial segmentation of a CGH, frequency-transformation with 2D-DCT (2-dimensional discrete cosine transform), and motion estimation and residual image generation in the frequency-domain. It uses H.264/AVC, the lossless compressor BinHex, and a linear quantizer that we have made. From the experiments the proposed scheme showed the image quality of about 25.4 dB at the compression ratio of 10:1 and about 16.5dB at 90:1 compression ratio.

In this paper, a method for lossless and near lossless compression of large digital mammograms is proposed. This method is based on a predictive coder that uses integer-to-integer operations, and generates a two-layer embedded bit stream (i.e. near-lossless and refinement layer). The maximum tolerated pixel distortion is guaranteed in the near lossless component. Comparisons with other proposed approaches are based on a database of high-resolution 12 bits/pixel digital mammograms. The simulation results indicate that our lossless compression method can offer average bit rates 39, 5%, 24%, 7% and 2.5% better than PNG, JPEG 2000, JPEG-lossless, and LOCO, respectively. Besides, for the same images, the proposed method offers near lossless compression at relatively low computational cost, providing an average bit rate of 2.57 bits/pixel and PSNR of 47.77 dB.

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as Wavelet Neural Networks (WNN) and genetic algorithms are being developed to explore the future of image coding. Successful applications of WNN in the case of function approximation become well established. This paper presents a direct solution method based on Multi Library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.

In this paper we propose a DWT-based perceptually lossless image compression algorithm and architecture. Many image-capturing devices acquire images in subsampled R-G-B format and have real-time constraints which prohibit conversion to other formats, generally preferred for image compression, such as Y-Cb-Cr. Therefore, we propose a compression algorithm which works performed directly on the subsampled R-G-B data. The scheme exploits the correlation between the color planes and the perceptual sensitivity of the human visual system in order to improve the compression ratio. Moreover it can be easily mapped into an integrated fast architecture yielding very high throughput.