Robust medical image compression based on wavelet transform and vector quantization

Image-Compression Techniques: Classical and "Region-of-Interest-Based" Approaches Presented in Recent Papers

Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. This paper proposes a study of relevant papers from the last decade which are focused on the selection of a region of interest of an image and on the compression techniques that can be applied to that area. To better highlight the novelty of the hybrid methods, classical state-of-the-art approaches are also analyzed. The current work will provide an overview of classical and hybrid compression methods alongside a categorization based on compression ratio and other quality factors such as mean-square error and peak signal-to-noise ratio, structural similarity index measure, and so on. This overview can help researchers to develop a better idea of what compression algorithms are used in certain domains and to find out if the presented performance parameters are of interest for the intended purpose.

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques

A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of compressed image and video data can greatly reduce input data size and eliminate the need for decompression and recompression, thereby achieving significant savings in memory and computation time. There exists a body of research on compression domain data processing and analysis. This survey focuses on the work related to image and video data. The papers cited are categorized based on their target applications, including image and video resizing and retrieval, information hiding and watermark embedding, image and video enhancement and segmentation, object and motion detection, as well as pattern classification, among several other applications. Key methods used for these applications are explained and discussed. Comparisons are drawn among similar approaches. We then point out possible directions of further research.

Efficient Storage and Encryption of 32-Slice CT Scan Images Using Phase Grating

Medical images are treated as sensitive as it carries patients' confidential information and hence must be protected from unauthorized access. So, a strong encryption mechanism is a primary criterion to transmit these images over the internet to protect them from intruders. In many existing algorithms, noise affection in the extracted images is high, hence not suitable for medical data encryption. Here, we present a new method using phase grating to multiplex as well as encrypting 32 cross-sectional CT scan images (slices) in a single canvas for optimization of storage space and improvement of security. The entire process is divided into a few steps. Before transmission, the main canvas is encrypted with the help of a random phase matrix. The main canvas is further encrypted by the transposition method to enhance security. After decryption, inverse Fourier transform is applied at the proper location of the decrypted canvas to extract the images from the spectra. Quality is measured with peak-signal-to-noise ratio and correlation coefficient methods. Here, it is greater than 38 and the correlation coefficient is close to 1 for all images, thereby indicating of good quality of extracted images. The effect of three common cyber-attacks (viz. known-plaintext attack, chosen-plaintext attack, and chosen-ciphertext attack) is also presented here. The correlation coefficient during cyber-attacks is found to be close to zero, which implies the robustness of the algorithm against cyber-attacks. Finally, a comparison with existing techniques shows the effectiveness of the proposed method.

DWT Lifting Scheme for Image Compression with Cordic-Enhanced Operation

This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures.

Metric Embedding Learning on Multi-Directional Projections

Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision—mostly driven by deep learning—have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same; nature-inspired multi-layered convolutional nets learn the representations, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters.