Amplitude-Guided Phase Masking for Hologram Compression

Purpose: Maintaining visual acuity in glaucoma patients is an important part of preventing the deterioration of quality of vision. We identified specific areas of the papillomacular bundle (PMB) that were strongly associated with visual acuity, based on en-face images derived from optical coherence topography (OCT) wide scans. Methods: The study recruited 23 eyes of 21 glaucoma patients (age: 61.3 ± 13.0 years, M: F = 9:12, Humphrey field analyzer-measured mean deviation: -19.9 ± 6.5 dB) with good best-corrected visual acuity (20/20 or more) and a remaining PMB with a maximum width no more than half that of the vertical disc diameter. En-face images were derived from 12 × 9 mm wide-scan images made with DRI-OCT (Triton, Topcon). Averaged en-face images were created by identifying the disc center and fovea line (DFL) and aligning it between images. We then measured the frequency of remaining PMB at 10 µm intervals along a vertical line intersecting the DFL at its midpoint. Finally, we used a logistic analysis in a much larger group of patients to identify cases of glaucoma with low BCVA (<20/20). Results: In the averaged en-face image, the residual PMB area appeared as a high-intensity region above the DFL. Analysis showed that residual PMB was most common in an area 830–870 µm above the DFL. The correlation coefficient of residual PMB in this area to BCVA was -0.57 (p < .01), and among OCT parameters in this residual PMB area, the AUC to identify decreased BCVA was highest for ganglion cell complex thickness (0.85, p < .01), with a cutoff of 87.5 µm. Conclusions: This study identified specific areas of the PMB that were associated with BCVA in wide-scan, en-face OCT images from glaucoma patients. This suggests that it may be possible to identify visual impairment during glaucoma treatment by measuring this area.

This paper presents a method for reconstructing an acoustic source located in a two-layered medium from multi-frequency phased or phaseless far-field patterns measured on the upper hemisphere. The interface between the two media is assumed to be flat and infinite, while the source is buried in the lower half-space. In the phased case, a Fourier method is proposed to identify the source based on far-field measurements. This method assumes that the source is compactly supported and can be represented by a sum of Fourier basis functions. By utilizing the far-field patterns at different frequencies, the Fourier coefficients of the source can be determined, allowing for its reconstruction. For the case where phase information is unavailable, a phase retrieval formula is developed to retrieve the phase information. This formula exploits the fact that the far-field patterns are related to the source through a linear operator that preserves phase information. By developing a suitable phase retrieval algorithm, the phase information can be recovered. Once the phase is retrieved, the Fourier method can be adopted to recover the source function. Numerical experiments in two and three dimensions are conducted to validate the performance of the proposed methods.

Video analysis is large source of unstructured information today. Introduction to HEVC standard has tremendous potential to lower the cost of data transfer and storage. HEVC provides forty percent or more than that of reduction in bit rate at the same visual quality. Looking over problem of limited bandwidth it became necessary to compress video data. To compress the data spatial and de correlated data is removed. This causes compacted information to fill in as a key asset for machine learning with altogether less examples for preparing. In this venture, a great way to deal with closer view extraction/division is expected abuse novel spatio-transient de-related piece alternatives taken straightforwardly through HEVC compressed video. Various techniques, in distinction, work on pictures which are uncompressed providing vital storage and machine resources not just for the decryption method before data formatting. The planned approach has been quantitatively and qualitatively evaluated against Gaussian mixture model.

Methods of Compression of Digital Holograms

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

Steganography algorithms used to secure sensitive data on the Internet. It hides the secret data in the cover image and provides imperceptibility to the attacker. The least significant bit (LSB) is the most preferred data hiding technique in the steganography. In this technique, the least significant bit of the cover image pixel replaced with data bits. In the literature, the data hiding achieved without considering the human visual system (HVS) characteristics that degrade the visual quality of the image. In this paper, the optimized data hiding has done while considering the HVS characteristics. The colour image contains three planes known as the red–green–blue plane. The green plane is the most sensitive, and blue is less sensitive to the human eyes. Thus, in the proposed technique, the green plane used as a reference plane for data hiding in the red and blue planes. In the optimized data hiding technique, the secret data bits match with the cover pixel bits. If the bits match, then the corresponding optimal index determines, else data hiding done in the LSB bits of the cover pixel. After that, the optimal indexes hide in the cover image using a 2-bit LSB technique. In the proposed technique, image smooth and edge region characteristics explored before data hiding. There is a high correlation between the consecutive pixels in the smooth region and the minimum correlation on the edges. Thus, we have matched the secret data bits with the cover pixel bits in the smooth region and hide the optimal indexes on the edges. The experimental results performed on the standard dataset images downloaded from the USC-SIPI image database. The experimental results show that the proposed technique provides better visual quality as compared to the existing techniques.

In the fault diagnosis of rolling element bearings (REBs), spall size is a typical indicator of fault severity. Conventionally, spall size estimation relies on expert-knowledge-based or data-driven approaches. Expert-knowledge-based approaches require accurate assumptions about spall-excited events, making them challenging to apply in field environments. In contrast, data-driven approaches often struggle with insufficient training data and limited generalization across various operating conditions. To address this challenge, this paper proposes a frequency-enhanced neural network (FENN) with a hybrid spall-size estimator (HSSE). The proposed FENN employs both one-dimensional convolution in the time domain and Fourier convolution on the frequency magnitude, while preserving phase information in the frequency domain to enhance frequency components that are associated with spall in REBs. The novel HSSE proposed here integrates a data-driven spall-size estimator and an expert-knowledge-guided spall-size estimator to capture spall entry and exit events between rolling elements and raceways. Model validation results, which analyzed both simulation and experimental data from roller and ball bearings, demonstrate that the proposed approach provides accurate predictions of spall size, even with limited training data. Additionally, it is confirmed that the proposed model identifies the mechanical frequencies associated with spall events, providing interpretable results from raw vibration signals without requiring further processing.

This study updates a body of literature that aims to separate atmospheric disturbances into standing and traveling zonal wave components. Classical wavenumber–frequency analysis decomposes longitude- and time-dependent signals into contributions from distinct spatial and temporal scales. Here, an additional decomposition of the spectrum into standing and traveling components is described. Previous methods decompose the power spectrum into standing and traveling parts with no explicit allowance for covariance between the two. This study provides a simple method to calculate the variance of each of these components and the covariance between them. It is shown that this covariance is typically a significant portion of the variance of the total signal. The approach also preserves phase information and allows for the reconstruction of the real-space standing and traveling components. The technique is applied to reanalysis wintertime geopotential height anomalies in the Northern Hemisphere in order to investigate planetary wave interference effects in stratosphere–troposphere coupling. The results show that for planetary waves 1–3, standing waves explain the largest portion of the variance at low frequencies. An exception is for wave 1 in the high-latitude troposphere, where there is a strong westward-traveling wave. Furthermore, the antinodes of the standing waves have preferred longitudes that tend to align with the extremes of the climatological wave, suggesting that standing waves contribute to a linear interference effect that has been shown to be an important part of stratosphere–troposphere interactions.

Few-shot learning (FSL) techniques have been introduced to address the challenge of limited datasets in mechanical fault diagnosis. However, most existing FSL methods primarily focus on input–output correlations and neglect causal relationships, which limits the interpretability and robustness of diagnostic results. To tackle this issue, we propose a causal disentanglement few-shot relation metric network for mechanical fault diagnosis, comprising feature encoding, causal intervention, causal disentanglement, and relation metric modules. The causal intervention module performs linear interpolation on amplitude information (encoding low-level statistics) while preserving phase information (encoding high-level semantics) to intervene causally on the frequency-domain image. Fault features are extracted via the feature encoder module, and a factor disentanglement loss in the causal disentanglement module transforms them into independent causal features with explicit causal relationships. The relation metric module learns pairwise causal feature distances through meta-task training, thus constructing a trainable similarity metric space. This approach can effectively capture the differences in causal fault features between samples, enhancing the interpretability and generalization ability of the model. Experiments on both public and laboratory datasets demonstrate superior performance over state-of-the-art methods.

The terahertz (THz, 0.1–10 THz) band has garnered significant attention in biomedical imaging and material inspection due to its unique fingerprint characteristics and non-ionizing nature. However, conventional THz time-domain spectroscopy (THz-Tds) imaging is fundamentally constrained by the diffraction limit, making sub-wavelength resolution imaging challenging. This work presents a THz super-resolution imaging method utilizing spintronic THz emitter (STE), enabling micron-scale spatial resolution while preserving phase information. A femtosecond laser pulse excites the STE, and a knife-edge method is used to determine a spatial resolution of 2.8 μm, below λ/100 at 1 THz.

A compression method is presented which combines the distinct advantage of being fixed length with the visual picture quality of transform-based coding methods. This method compresses the Karhunen-Loeve transform coefficients of 8/spl times/8 pixel blocks of the picture and attains a compression ratio of 15.37:1 for color images. Compared to the absolute moment block truncation coding, the other known fixed length compression method, our method improves the compression ratio by 68% and significantly improves the visual quality of the reconstructed picture.

The high efficient video coding (HEVC) standard is crucial for video processing and communication. It is able to achieve about 50% bit-rate reduction with equivalent visual quality compared to H.264/AVC. In the in-loop filter part of HEVC, sample adaptive offset (SAO) is newly added in order to improve the coding efficiency as well as the subjective quality of encoded videos. In order to improve visual quality, we propose an approach by using convolutional neural networks (CNN) after de-blocking filter (DBF) to replace SAO, namely SAOCNN in this paper. Compared with SAO, the proposed SAOCNN is able to reduce the distortion of reconstructed pixels by learning residual feature. Further more, there will be no bit-rates by using the same SAOCNN model in both encoder and decoder sides. Experimental results show that our proposed approach achieves an average 2.04% BD-rate reduction as well as the average 0.12 BDPSNR increment for All intra configuration with sacrifice of encoding time rarely.

In this paper, we address the dependent joint bit allocation problem in H.264/AVC statistical multiplexing. For most of the existing methods, in order to improve the overall visual quality, the bit allocation is performed upon the relative complexities of different video programs. However, due to the temporal prediction employed in H.264, the influence of current frame to the rate-distortion (R-D) performances of the future frames should be taken in to account as well. The contributions of the paper are two-fold. First, a simple but accurate inter-frame dependency model (IFDM) is introduced which can quantitatively measure the coding dependency between the current coding frame and its reference frame. Second, based on the IFDM, the dependent joint bit allocation problem is revisited, and both the frame complexity and inter-frame dependency are considered in the bit allocation process. Then, it is proved that the dependent joint bit allocation problem can actually be relaxed into a convex optimization problem which can be optimally and efficiently solved. Experimental results demonstrate that the proposed dependent bit allocation method achieves 33.45% and 11.10% bitrate reduction on average compared with the equivalent bit allocation (EBA) and the optimal independent joint bit allocation (OIJBA) methods respectively.

Advanced inter-prediction modes are introduced recently in literature to improve video coding performances of both H.264 and High Efficiency Video Coding standards. Decoder-side motion analysis and motion vector derivation are proposed to reduce coding costs of motion information. Here, we introduce enhanced skip and direct modes for H.264 coding using decoder-side super-resolution (SR) and frame interpolation. P- and B-frames are downsampled and H.264 encoded at lower resolution (LR). Then reconstructed LR frames are super-resolved using decoder-side motion estimation. Alternatively for B-frames, bidirectional true motion estimation is performed to synthesize a B-frame from its reference frames. For P-frames, bicubic interpolation of the LR frame is used as an alternative to SR reconstruction. A rate-distortion optimal mode selection algorithm is developed to decide for each MB which of the two reconstructions to use as skip/direct mode prediction. Simulations indicate an average of 1.04 dB peak signal-to-noise ratio (PSNR) improvement or 23.0% bitrate reduction at low bitrates when compared with H.264 standard. The PSNR gains reach as high as 3.00 dB for inter-predicted frames and 3.78 dB when only B-frames are considered. Decoded videos exhibit significantly better visual quality as well.

A method for fast and low bit-rate compression of digital holograms based on a new vector quantization (VQ) method known as the skip-dimension VQ (SDVQ) is proposed. Briefly, a complex hologram is converted into a real off-axis hologram, and partitioned into a set of image vectors. The image vectors are passed into a graphic processing unit (GPU), and compressed through SDVQ into a set of code indices considerably smaller in data size than the source hologram. Experimental evaluation reveals that our scheme is capable of compressing a digital hologram to a compression ratio of over 500 times, in approximately 20-22 ms.