A Linear Color Correction Method for Compressed Images and Videos

A brightness adjustment special effects editing method in MPEG-2 compressed domain is proposed. The method is realized in the DCT domain of YUV color space without full frame decompression. Experimental results show that the visual quality of the edited video in the compressed domain is identical to the one edited in the uncompressed domain.

Cameras record three color responses (RGB) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZ-useful for color measurement-by a mapping function, e.g., the simple 3×3 linear transform (usually derived through regression). This mapping, which we will refer to as linear color correction (LCC), has been demonstrated to work well in the number of studies. However, it can map [Formula: see text] to XYZs with high error. The advantage of the LCC is that it is independent of camera exposure. An alternative and potentially more powerful method for color correction is polynomial color correction (PCC). Here, the R, G, and B values at a pixel are extended by the polynomial terms. For a given calibration training set PCC can significantly reduce the colorimetric error. However, the PCC fit depends on exposure, i.e., as exposure changes the vector of polynomial components is altered in a nonlinear way which results in hue and saturation shifts. This paper proposes a new polynomial-type regression loosely related to the idea of fractional polynomials which we call root-PCC (RPCC). Our idea is to take each term in a polynomial expansion and take its k th root of each k -degree term. It is easy to show terms defined in this way scale with exposure. RPCC is a simple (low complexity) extension of LCC. The experiments presented in this paper demonstrate that RPCC enhances color correction performance on real and synthetic data.

Error compensation is one of effective and economical means to improve the machining accuracy of machine tools. The measurement accuracy of kinematic errors has great impact on the accuracy of machine tool error modeling. Some measurement errors are inevitably generated due to the distortion of the slide table and the limit of local measuring stroke. To solve these problems, an improved machine tool volumetric error compensation method is proposed in this paper, based on linear and squareness error correction method. The linear errors, including position errors and straightness errors, are corrected by considering the distance effect between optical mirror groups and the surface of worktable. The squareness errors are corrected through modifying local squareness errors measured with double ball bar to global squareness errors, with the aid of straightness errors measured with multi-laser calibrator. Then volumetric errors are obtained based on multi-body system theory, and volumetric diagonal errors are measured with laser interferometer. It is illustrated that the bidirectional systematic deviation of positioning of four volumetric diagonals reduced about 81.3%, 35.2%, 29.2%, and 4.5% respectively by using this improved machine tool volumetric error compensation method, and it is of obvious advantages in contrast with traditional compensation method with direct measurement data. The improved error compensation method proposed in this paper has universal applicability and has great significance on machine tool accuracy improvement.

Images captured in the sand–dust weather often suffer from serious colour cast and poor contrast, and this has serious implications for outdoor computer vision systems. To address these problems, a normalised gamma transformation‐based contrast‐limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand–dust image enhancement is proposed in this study. This method consists of image contrast enhancement and image colour correction. To avoid producing new colour deviation, the input sand–dust images are first transformed from red, green, and blue colour space into Lab colour space. Then, the contrast of the lightness component (L channel) of the sand–dust image is enhanced using CLAHE. To avoid unbalanced contrast, as well as to reduce the overincreased brightness caused by CLAHE, a normalised gamma correction function is introduced to CLAHE. After that, the a and b chromatic components are recovered by a grey‐world‐based colour correction method. Experiments on real sand–dust images demonstrate that the proposed method can obtain the highest percentage of new visible edges for all testing images. The contrast restoration exhibits good colour fidelity and proper brightness.

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.

Предмет изучения. В статье предложены новые методы и средства цветовой коррекции web-камер в системах стереозрения для повышения качества их работы. Целью является сопоставительный анализ показателей качества известных методов коррекции цвета, разработка новой методики и рабочих алгоритмов совместной процедуры цветовой коррекции и ректификации фреймов видеопотока левой и правой камер. Задачи: Ставилась задача выполнить теоретический анализ показателей качества известных алгоритмов коррекции цвета, разработать новые рабочие алгоритмы, программные коды этих алгоритмов написать на языке Python c использованием необходимых функций OpenCV. Провести экспериментальные исследования этих алгоритмов. Оценить эффективность работы стереосистемы в лабораторных условиях, а достоверность полученных результатов проверить методами статистического анализа. Используемые методы: Сопоставительный анализ известных методов и алгоритмов путем статистического моделирования, синтез новых алгоритмов и оценка эффективности их работы путем проведения лабораторных натурных испытаний. Полученные результаты: проведен сопоставительный анализ эффективности работы известных методов цветовой коррекции камер стереосистемы, предложены новые более эффективные алгоритмы для решения этой задачи. Выводы. Научная новизна полученных результатов: предложены новые методы коррекции цветового баланса web-камер, используемых в стереоскопических системах технического зрения, отличающиеся высокой точностью цветовой коррекции и работающих в реальном масштабе времени с применением функций библиотеки OpenCV в программной среде Python.

The increased amount of visual data in several applications necessitates sophisticated indexing techniques for retrieval based on the image content. The recent JPEG2000 standard addresses the need for content based coding and manipulation of visual media. Future multimedia databases are expected to store images in the JPEG2000 format. Hence, it is crucial to develop indexing and retrieval systems that operate in the framework. Several content based indexing techniques have been proposed in the wavelet domain which is the technique of choice in the JPEG2000 standard. However, most of these techniques rely on extracting low level features such as color, texture and shape that represent the global image content. In this paper, we propose a region based indexing technique in the JPEG2000 framework. Specific regions of interest (ROI) (which insure the reconstructed quality in the image) are tracked and analyzed through different layers of the wavelet transform in the coding process. Shape features are extracted from the ROI sketch in the uncompressed domain. Texture and color features are extracted in the compressed domain at different wavelet resolutions corresponding to these regions. Indexing and retrieval are based on a combination of these features. Extensive simulations have been performed in the JPEG2000 framework. Experimental results demonstrate that compared to the exiting wavelet based indexing approaches the proposed scheme has superior performance.

This paper presents two different watermarking schemes devoted to protect video objects. The first presented scheme performs embedding and detection in the uncompressed domain. It has been developed to enable signature detection after object manipulations such as rotations, translations and VOL modifications. To achieve these requirements, the first scheme exploits the shape of the object using CPA analysis: a random sequence is transformed to fit the scale and the orientation of the object. The detection of the mark is performed applying an inverse transform and calculating a correlation between the random sequence and the transformed object. The second scheme is based on compressed-domain processing of video objects. Two different signals are embedded, one for synchronization recovery and another for copyright protection. Both signals are embedded and detected in the compressed domain. During detection, first synchronization recovery is performed and then the copyright watermark is extracted.

Relaxometric measurement of the effective transverse relaxation rate R2* plays an important role in the quantitative evaluation of brain function, perfusion, and tissue iron content. However, accurate measurement of R2* is prone to macroscopic background field inhomogeneity. In clinical applications and systems, postprocessing correction techniques are more flexible in implementation than unsupported protocol or hardware modifications. The current postprocessing correction approach assumes the cross-slice background field inhomogeneity can be approximated by a linear gradient and corrects for a sinc modulation function. The importance of the high-order terms in background field inhomogeneity has increased with the fast development of high- and ultrahigh-field scanners in recent years. In this study, we derived an analytical expression of the free induction decay signal modulation in the presence of a quadratic cross-slice background field inhomogeneity. The proposed quadratic correction method was applied to phantom and volunteer studies and demonstrated to be superior to the classic monoexponential model, monoexponential-plus-constant model, and the linear sinc correction method in recovering background field inhomogeneity-induced. R2* overestimations with visual inspection of R2* parametric maps and a statistical model selection technique. We also tabulated 7-T T2*/R2* measurements of several human brain structures and MnCl(2) solutions with various concentrations for fellow researchers' reference.

Motivation: Nyquist ghost artifacts and fuzzy ripple artifacts are more pronounced in high-resolution fMRI in 7-Tesla, which may introduce bias into fMRI signal. Goal(s): The target of this study is to develop a technique for multi-shot-EPI-based sub-millimeter fMRI, which can correct the artifacts without reducing temporal resolution. Approach: We developed multi-shot dual-polarity GRAPPA (msh-DPG) which is a combination of DPG and traditional GRAPPA. Results: Msh-DPG can effectively correct the Nyquist ghosts and reduce the fuzzy ripple artifacts. It outperforms the linear phase correction method. Impact: The proposed method can correct or reduce two common artifacts in sub-millimeter fMRI without reducing the temporal resolution. It may help to improve the image quality and stability of high-resolution fMRI, especially in ultra-high-field systems.

Accurate detection of drug-induced QT interval changes is often confounded by concurrent heart rate changes. Application of heart rate correction formulas has been the traditional approach to account for heart rate-induced QT interval changes, and thereby identify the direct effect of the test article on cardiac repolarization. Despite numerous recent studies identifying the imprecision of these formulas they continue to be applied. Using a chronic atrioventricular dissociated His-paced canine model, heart rate correction methods were evaluated for their ability to generate a corrected QT interval independent of original heart rate. Additionally, His bundle pacing at a heart rate of 60 beats/min allowed calculation of the magnitude of error introduced by application of heart rate correction formulas. Of the fixed parameter heart rate correction formulas, only Van de Water was able to predict corrected QT values independent of the original heart rate. The magnitude of error discovered by application of heart rate correction formulas varied, but in many cases was very large. Bazett's formula was associated with a mean overcorrection of 67.9 ms; Fridericia's 28.7 ms. Van de Water was the best fixed parameter formula with a mean error of 10.8 ms. As expected, group and individual corrections derived from linear regression of the HR-QT data offered improvement over the traditional formulas. Both were able to predict QTc values independent of the heart rate. However, errors of the magnitude of 10 and 6 ms, respectively, were still introduced. Van de Water and linear regression correction methods were superior to others in this study, but all methods generated QTc errors equal to or much greater than the magnitude of interest for drug safety evaluation.

The present study is conducted to assess the influence of climate model biases in the predictions of yield and water requirement of cassava in one of the major cassava growing regions in India. Simple linear bias correction methods are used for temperature, and non-linear corrections are used for other meteorological variables. The WOFOST and CROPWAT models are used to predict the crop yield and water requirement of cassava using the scenarios of 2030, 2050, and 2070 for the representative concentration pathway 4.5 derived from the Long Ashton Research Station Weather Generator (LARS-WG). The percentage change in crop yield predictions with and without bias corrections of meteorological variables ranges from 7.6 to 10.8%, 1.6 to 5.4%, and − 3.0 to 4.0% respectively for 2030, 2050, and 2070. The bias corrections made an increment in the gross irrigation requirements of cassava with 16.5, 17.8, and 16.0% in 2030, 2050, and 2070 respectively, compared to the values without bias corrections. The outcome of this study indicates that raw meteorological variables directly from the climate models result over-/underestimation of yield and irrigation requirements of cassava, and the bias corrections help to issue reliable crop yield predictions. Results show zero yield reductions of cassava until 2050, and beyond that, there can be reductions in the crop yield. The gross irrigation requirements of cassava increase in the future to achieve higher productivity. However, this study needs to extend to other major growing regions in India to derive a general conclusion.

CDK2 activity crosstalk on the ERK kinase translocation reporter can be resolved computationally.

Low-light image enhancement (LLIE) aims to improve the visibility and perceptual quality of images captured under low-light conditions, enhancing as much structure and detail as the captured data allow. Existing methods often rely on deep neural networks to map low-light images to normal-light counterparts within color spaces such as standard red green blue (sRGB) or hue-saturation-value (HSV). However, these approaches are prone to noise interference and lack robustness, resulting in noticeable artifacts in color and brightness. To address these challenges, we propose task-decoupled framework for low-light image enhancement (TDNet), a task-decoupled LLIE framework that regards LLIE as a multi-task learning problem: denoising and color correction, enabling targeted optimization for each sub-task. Specifically, the denoising sub-task further decouples noise suppression across three channels and employs a fine-denoise channel attention module to effectively handle channel-specific noise heterogeneity. Simultaneously, a cross-channel fusion attention module captures global semantic information and inter-channel dependencies, enhancing color consistency and ensuring natural visual aesthetics. In addition, a progressive detail recovery strategy is incorporated to refine structural clarity and edge details. Extensive experiments on multiple benchmark datasets demonstrate that TDNet achieves favorable performance in noise suppression and color correction while maintaining low computational complexity and parameter efficiency (https://github.com/634949684aa/TDNet).

The compressed domain video watermarking has been less expensive since complete decoding and then re-encoding is not required for watermark embedding. Handling synchronization error due to embedding is a challenging task, especially, due to motion compensation. In this paper, we propose an embedding scheme that can minimize desynchronization in the watermark extraction process at the decoder for embedding in compressed videos with better visual quality. Experimental results show the effectiveness of the proposed scheme.