Efficient Lossy Research Articles

Review of Image Quality Assessment Methods for Compressed Images.

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs.

Compact and efficient lossy mode resonant refractive index sensor for aqueous environment

A very short length high sensitivity, large figure of merit and very high resolution integrated-photonic refractometer for aqueous environment operating in visible region of wavelength is proposed. The sensor design depends on the periodic coupling of the guided dielectric optical waveguide mode field and the lossy mode field of the conducting indium-tin-oxide thin film. Various layer thicknesses can be optimized to provide power transfer to the lossy layer and lossy mode resonance, resulting in a strong guided mode power absorption in the lossy layer occurs. The sensor has been designed to operate in both the TE and TM polarizations with different optimized layer frthicknesses. The optimized thicknesses are different for TE and TM polarizations. The obtained numerical results show that a spectral sensitivity of 2200 nm/RIU/2645 nm/RIU for TE/TM mode could be achieved with a very high resolution. Also, the sensors can operate in power interrogation mode with a maximum sensitivity nearly 5 × 107 dB RIU−1.

Lossy P-LDPC Codes for Compressing General Sources Using Neural Networks

It is challenging to design an efficient lossy compression scheme for complicated sources based on block codes, especially to approach the theoretical distortion-rate limit. In this paper, a lossy compression scheme is proposed for Gaussian and Laplacian sources. In this scheme, a new route using "transformation-quantization" was designed to replace the conventional "quantization-compression". The proposed scheme utilizes neural networks for transformation and lossy protograph low-density parity-check codes for quantization. To ensure the system's feasibility, some problems existing in the neural networks were resolved, including parameter updating and the propagation optimization. Simulation results demonstrated good distortion-rate performance.

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.

Invertible Color-to-Grayscale Conversion Using Lossy Compression and High-Capacity Data Hiding

Invertible color-to-grayscale conversion is a research issue in grayscale image colorization, which is a complex problem resulting from information loss. This paper presents an innovative invertible color-to-grayscale conversion idea by independently compressing the chromaticity plane and hiding it into the corresponding luminance plane as a watermark. Since the chromaticity and luminance planes are orthogonal in the proposed method, they can be processed efficiently without mutual influence. By using an efficient lossy compression operation, we can save more chromatic information. By using two high-capacity data hiding techniques (reversible watermarking (RW) and least significant bit (LSB) substitution), we can embed the compressed chromatic information into the luminance plane perfectly. For the purpose of integrality authentication, the luminance plane is hashed as part of the embedded information before the embedding. Experimental results have shown that higher quality of reconstructed color images can be achieved by using RW, but the quality of the synthesized grayscale drops sharply. By using LSB substitution, we can obtain high-quality synthesized grayscale and reconstructed color images simultaneously. Furthermore, we have compared the proposed LSB-based scheme with several recently reported state-of-the-art methods to validate the superiority of the proposed approach.

Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming

In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques.

Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net

Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net

Lossy Point Cloud Geometry Compression via Region-Wise Processing

Point cloud geometry (PCG) is used to precisely represent arbitrary-shaped 3D objects and scenes, is of great interest to vast applications which puts forward the pressing desire of high-efficiency PCG compression for transmission and storage. Existing PCG coding mostly relies on the octree model by which point-wise processing is applied without exploring nonlocal regional geometry similarity across the entire 3D surface. This work, instead, suggests the region-wise processing to leverage the region similarity to exploit inter-region redundancy for efficient lossy point cloud geometry compression. Towards this goal, a given PCG is first segmented into numerous local regions each of which comprises a portion of point cloud surface, and can be represented by a surface vector that describes the geometry shape numerically in a projected principal space. Subsequently, these regions are grouped into several discriminative clusters, assuring that inter-cluster similarity is minimized and intra-cluster similarity is maximized simultaneously, where the similarity is calculated using the regional surface vectors. In each cluster, we set a reference region having the largest similarity score to the others, which enables the non-reference region prediction from the reference one using alignment transform. In the end, we encode the reference regions directly using the lossless mode of the Geometry-based Point Cloud Compression (G-PCC), while corresponding non-reference regions are signaled using associated transform parameters. Compared with the state-of-the-art G-PCC using octree model, our region-wise approach can offer remarkable coding efficiency improvement, e.g., 32.4% and 22.0% Bjontegaard-delta rate (BD-Rate) gains for respective point-to-point ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D1$ </tex-math></inline-formula> ) and point-to-plane ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D2$ </tex-math></inline-formula> ) distortion evaluations, across a variety of common test sequences used in standard committee.

Lossy Compression using Adaptive Polynomial Image Encoding

In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the nu ...

Iterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Images

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods.

An efficient lossy cartoon image compression method

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

Sensitivity of Seizure Pattern Prediction to EEG Signal Compression

This paper presents a framework for Electroencephalography (EEG) seizure prediction in time domain. Moreover, it studies an efficient lossy EEG signal compression technique and its effect on further processing for seizure prediction in a realistic signal acquisition and compression scenario. Compression of EEG signals are one of the most important solutions in saving speed up signals transfer, reduction of energy transmission and the required memory for storage in addition to reduction costs for storage hardware and network bandwidth. The main objective of this research is to use trigonometric compression techniques including; Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) algorithms on EEG signals and study the impact of the reconstructed EEG signals on its seizure prediction ability. Simulation results show that the DCT achieves the best prediction results compared with DST technique achieving sensitivity of 95.238% and 85.714% respectively. The proposed approach gives longer prediction times compared to traditional EEG seizure prediction approaches. Therefore, it will help specialists for the prediction of epileptic seizure as earlier as possible.

Graph-Based Static 3D Point Clouds Geometry Coding

Recently, 3D visual representation models such as light fields and point clouds are becoming popular due to their capability to represent the real world in a more complete and immersive way, paving the road for new and more advanced visual experiences. The point cloud representation model is able to efficiently represent the surface of objects/scenes by means of a set of 3D points and associated attributes and is increasingly being used from autonomous cars to augmented reality. Emerging imaging sensors have made it easier to perform richer and denser point cloud acquisitions, notably with millions of points, making it impossible to store and transmit these very high amounts of data without appropriate coding. This bottleneck has raised the need for efficient point cloud coding solutions in order to offer more immersive visual experiences and better quality of experience to the users. In this context, this paper proposes an efficient lossy coding solution for the geometry of static point clouds. The proposed coding solution uses an octree-based approach for a base layer and a graph-based transform approach for the enhancement layer where an Inter-layer residual is coded. The performance assessment shows very significant compression gains regarding the state-of-the-art, especially for the most relevant lower and medium rates.

Lossless Image and Intra-Frame Compression With Integer-to-Integer DST

Video coding standards are primarily designed for efficient lossy compression, but it is also desirable to support efficient lossless compression within video coding standards using small modifications to the lossy coding architecture. A simple approach is to skip transform and quantization, and simply entropy code the prediction residual. However, this approach is inefficient at compression. A more efficient and popular approach is to skip transform and quantization but also process the residual block with DPCM, along the horizontal or vertical direction, prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms can map integer pixels to integer transform coefficients without increasing the dynamic range and can be used for lossless compression. We focus on lossless intra coding and develop novel i2i approximations of the odd type-3 DST (ODST-3). Experimental results with the HEVC reference software show that the developed i2i approximations of the ODST-3 improve lossless intra-frame compression efficiency with respect to HEVC version 2, which uses the popular DPCM method, by an average 2.7% without a significant effect on computational complexity.

An Optimized Quantization Technique for Image Compression Using Discrete Tchebichef Transform

Discrete Tchebichef transform (DTT) has been utilized to improve the reconstruction quality of the traditional methods in image compression. Although DTT has the effective capability of energy concentration and ease of computation, not been exploited polynomials in orthogonal transform as compared with discrete cosine transform (DCT). This paper proposes an efficient lossy compression based DTT to produce better quality reconstructed image for the desired compression ratio. We combine soft decision quantization (SDQ) to design optimal quantization table and to approximate the rate-distortion for the purpose of the reconstruction quality. Compared with DCT under the scheme of JPEG baseline system, experimental results show that the proposed algorithm is of greater reconstruction image quality when the bit ratio exceeds 0.5 bpp. The bit ratio is decreased by 0.25, 0.49, 0.20 bpp, respectively when peak signal-to-noise-ratio (PSNR) is 35, 40, 45 dB. Meanwhile, they are similar on the elapsed time in encoding and decoding.

Volumetric Medical Images Lossy Compression using Stationary Wavelet Transform and Linde-Buzo-Gray Vector Quantization

The aim of the study is to reduce the size required for storage along with decreasing the bitrate and the bandwidth for the process of sending and receiving the image. It also aims to decrease the time required for the process as much as possible. This study proposes a novel system for efficient lossy volumetric medical image compression using Stationary Wavelet Transform and Linde-Buzo-Gray for Vector Quantization. The system makes use of a combination of Linde-Buzo-Gray vector quantization technique for lossy compression along with Arithmetic coding and Huffman coding for lossless compression. The system proposed uses Stationary Wavelet Transform and then compares the results obtained to Discrete Wavelet Transform, Lifting Wavelet Transform and Discrete Cosine Transform at three decomposition levels. The system also compares the results obtained using transforms with only Arithmetic Coding and Huffman Coding for Lossless Compression.The results show that the system proposed outperforms the others.

Adaptive compression of animated meshes by exploiting orthogonal iterations

We introduce a novel approach to support fast and efficient lossy compression of arbitrary animation sequences ideally suited for real-time scenarios, such as streaming and content creation applications, where input is not known a priori and is dynamically generated. The presented method exploits temporal coherence by altering the principal component analysis (PCA) procedure from a batch- to an adaptive-basis aiming to simultaneously support three important objectives: fast compression times, reduced memory requirements and high-quality reproduction results. A dynamic compression pipeline is presented that can efficiently approximate the k-largest PCA bases based on the previous iteration (frame block) at a significantly lower complexity than directly computing the singular value decomposition. To avoid errors when a fixed number of basis vectors are used for all frame blocks, a flexible solution that automatically identifies the optimal subspace size for each one is also offered. An extensive experimental study is finally offered, showing that the proposed methods are superior in terms of performance as compared to several direct PCA-based schemes while, at the same time, achieves plausible reconstruction output despite the constraints posed by arbitrarily complex animated scenarios.

An efficient lossy link localization approach for wireless sensor networks

Network fault management is crucial for a wireless sensor network (WSN) to maintain a normal running state because faults (e.g., link failures) often occur. The existing lossy link localization (LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hy-pothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination (CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nonde-terministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effec-tiveness of the proposed approach.

Block-based KLT compression for multispectral images

An efficient lossy compression algorithm for multispectral images based on block Karhunen–Loève transform (KLT) is proposed. First, a two-dimensional discrete wavelet transform is performed on each band of multispectral images to remove the spatial correlation. Subsequently, each band is partitioned into non-overlapping blocks of the same size, and the transform coefficients of each block in the wavelet domain are treated as a single object. Blocks that are co-located in the spectral orientation are affected by an adaptive Karhunen–Loève transform to remove their spectral correlation. Finally, embedded block coding with optimized truncation is performed on all principal components to generate the final bit-stream. Experimental results show that the proposed algorithm, based on block KLT, outperforms the algorithm based on global KLT, without significant increase of complexity.

Memory cost of absorbing conditions for the finite-difference time-domain method.

Three absorbing layers are investigated using standard rectilinear finite-difference schemes. The perfectly matched layer (PML) is compared with basic lossy layers terminated by two types of absorbing boundary conditions, all simulated using equivalent memory consumption. Lossy layers present the advantage of being scalar schemes, whereas the PML relies on a staggered scheme where both velocity and pressure are split. Although the PML gives the lowest reflection magnitudes over all frequencies and incidence angles, the most efficient lossy layer gives reflection magnitudes of the same order as the PML from mid- to high-frequency and for restricted incidence angles.