Visual Degradation Research Articles

External crosslinking of biopolymers on concrete for crack sealing under high pressure and increased durability applications

The durability of concrete structures is compromised by the presence of cracks, which can lead to further deterioration if left untreated. To prevent this, it is crucial to close the cracks and restore impermeability. This paper proposes an economical and practical solution: the application of biopolymers as an external treatment layer on horizontal concrete surfaces to seal cracks. The study explores the crosslinking of calcium alginates and gelatin at various concentrations. Several properties are investigated, including water permeability, freeze-thaw resistance, overall visual degradation, resistance to chloride intrusion, and adhesive strength of the repair layer. The findings indicate that repair layers utilizing calcium alginate demonstrate promising results for external crack sealing applications. These layers effectively restore impermeability, exhibit resistance against chloride intrusion, improve freeze-thawing resistance, while showing an acceptable adhesive strength to the horizontal concrete substrate. This suggests a viable solution for enhancing the durability of concrete structures.

Microscope-enabled Disc Dissolution System: Concordance between drug and polymer dissolution from an amorphous solid dispersion disc and visual disc degradation

A microscopic erosion time test was recently described to anticipate amorphous solid dispersion (ASD) drug load dispersibility limit, using 0.5ml media volume. Studies here build upon this microscope-enabled method but focus on drug and polymer dissolution from an ASD disc, along with imaging. The objective was 1) to design and build a microscope-enabled disc dissolution system (MeDDiS) with a 900mL dissolution volume and 2) assess the ability of MeDDiS imaging of dissolving discs to provide concordance with measured drug and polymer dissolution profiles. MeDDiS employed a digital microscope to image ASD discs and a one-liter vessel for dissolution. ASD discs containing ritonavir (5-50% drug load) and PVPVA were fabricated and subjected to in vitro dissolution using MeDDiS, where disc diameter was quantified with time. Ritonavir and PVPVA release were also measured. Results indicate concordance between imaging and dissolution. Both found 25% drug load to provide high drug and polymer release, but 30% yielded low release. Quantitatively, MeDDiS images predicted drug and polymer release profile, both above and below the drug load cliff. Overall, studies here describe a MeDDiS which has promised to anticipate drug and polymer dissolution, via imaging of dissolving discs, above and below the ASD drug load cliff.

On the Security of Selectively Encrypted HEVC Video Bitstreams

With the growing applications of video, ensuring its security has become of utmost importance. Selective encryption (SE) has gained significant attention in the field of video content protection due to its compatibility with video codecs, favorable visual distortion, and low time complexity. However, few studies consider SE security under cryptographic attacks. To fill this gap, we analyze the security concerns of encrypted bitstreams by SE schemes and propose two known plaintext attacks (KPAs). Then the corresponding defense is presented against the KPAs. To validate the effectiveness of the KPA, it is applied to attack two existing SE schemes with superior visual degradation in HEVC videos. Firstly, the video sequences are encoded using H.265/HEVC. During encoding, the selected syntax elements are recorded. Secondly, the recorded syntax elements are imported into the HEVC decoder. By utilizing the encryption parameters and the imported data in the decoder, it becomes possible to reconstruct a significant portion of the original syntax elements before encryption. Finally, the reconstructed syntax elements are compared with the encrypted syntax elements in the decoder, allowing the design of a pseudo-key stream (PKS) through the inverse of the encryption operations. The PKS is used to decrypt the existing SE scheme, and the experimental results provide evidence that the two existing SE schemes are vulnerable to the proposed KPAs. In the case of single bitstream estimation (SBE), the average correct rate of key stream estimation exceeds 93%. Moreover, with multi-bitstream complementation (MBC), the average estimation accuracy can be further improved to 99%.

Lactic and Acetic Acids in Sour Beers Promote Corrosion During Aluminum Beverage Can Storage

Newer sour beers are often packaged in aluminum cans, but the compatibility of sour beer and its major acids (lactic, acetic) with cans is unclear. In an initial study, commercial sour beers were packaged in cans containing one of four different liners (bisphenol A (BPA) epoxy, two BPA–non-intent (BPA-NI) epoxy, and acrylic). Corrosion, as measured by dissolved aluminum and visual degradation of the liner, was positively correlated with concentrations of lactic acid, acetic acid, and decreased pH value. After 48 wk, aluminum concentrations up to 58 mg/L were observed in one sour beer, or nearly 100-fold greater than typical dissolved aluminum concentrations in non-sour beers. Liner type did not affect corrosion. In a subsequent model sour beer study with two acrylic liners and one BPA-NI liner, molecular SO2 positively correlated with corrosion, but only at concentrations 5-fold higher than the maximum expected in sour beers. Other added components (chloride, copper, ethanol) did not affect corrosion. Addition of acetic, lactic, and phosphoric acid in varying equinormal combinations to a non-sour beer demonstrated that acetic and lactic acids (average dissolved aluminum = 2.54 mg/L following storage) promote corrosion more than phosphoric acid (average dissolved aluminum = 0.47 mg/L). Titratable acidity (TA) correlated well with corrosion, with increased dissolved aluminum observed at TA > 6 g/L as lactic acid equivalents. Organic acid corrosivity was hypothesized to relate to the proportion of acid in its neutral form, and thus these findings are relevant to producers of other beverages with high levels of organic acids.

No-reference Bitstream-based Perceptual Quality Assessment of Octree-Lifting Encoded 3D Point Clouds.

No-reference point cloud quality assessment (PCQA) based on bitstreams uses information extracted from the bitstream for quality monitoring at network nodes. We develop a no-reference PCQA model based on bitstreams for the perceived quality assessment of Octree-Lifting coded point clouds. At first, our research explores the essential correlation between subjective visual quality degradation and the texture quantization parameter (TQP) when using lossless geometric coding. Then, we enhance the proposed model by incorporating texture complexity (TC) while taking into account the dependence of perceptual coding distortion on the texture characteristics of a point cloud. We estimate TC by utilizing TQP and calculating the average standard deviation of the Y-component of the attribute value ( Y_ std), both of which are extracted from the bitstream. Then, a texture distortion assessment model is constructed based on TQP and Y_ std. The integration of the texture distortion model with the position quantization scale (PQS) results in the derivation of an overall no-reference bitstream-based PCQA model, named streamPCQ-OL. The findings from the conducted experiments highlight a significant superiority of the proposed model over existing approaches in terms of performance. The dataset and source code will be publicly released and made available for access at https://github.com/qdushl/Waterloo-Point-Cloud-Database-4.0.

The dependence of PurpleAir sensors on particle size distribution and meteorology and their relationship to integrating nephelometers

The National Park Service (NPS) has a need to measure real-time PM2.5 and haze levels across its many park units to inform the public of unhealthy air and track visual degradation of park scenic vistas. To fulfill this need, the NPS deployed PurpleAir monitors (PA) in several park units. PA monitors are inexpensive monitors promoted as particle counters capable of measuring particle size distributions. Each PA is equipped with two Plantower (PMS) particle counters and a temperature, relative humidity (RH) and pressure sensor. In 2022, the NPS initiated a field study to evaluate PA limitations, uncertainties, and how the measurements relate to particle light scattering. The study was conducted in Fort Collins, CO where five nephelometers, particle sizing instruments, semi-continuous ion concentration measurements, and seven PAs were deployed. One nephelometer and two PA's sampled air from an RH controlled chamber with RH ≤ 40%. The PA temperatures were accurate, but the PA RH measurements were systematically underreported RH by ∼28% and it is recommended to scale these data by 1.35. The PMS reported size distributions are shown to be invalid. PA instruments mounted without protection from wind exhibited biases of ±160% for wind speeds exceeding 5 mph. Hourly CH1 data were precise, with errors of ∼100% at low concentrations of 30 #/dl and 15% for concentrations ≥1000 #/dl. Normalizing all PMS sensors to each other reduced the errors to 20% and 8% for low and higher particle concentrations respectively. The reported total particle count, i.e. ≥0.3 μm channel (CH1), was biased low, and the bias decreased linearly with mean scattering diameter (MSD). The ratio of measured ≥0.3 μm to PA CH1 channel increases linearly with MSD. The overall normalized mean deviation (NMD) between the two measurements is 121.8%. The CH1 channel also increases as a function of RH primarily due to the growth of hygroscopic aerosols. On average, the relationship between the atmospheric scattering coefficient (bsp) and the PMS ≥0.3 μm CH1 channel is described by the equation bsp = 0.015 × CH1 for MSD levels less than 0.35 μm. The PMS CH1 response decreases linearly with MSD. The NMD between ambient PMS derived bsp and measured ambient bsp is 27.0%. Recommendations are made for PA network deployment.

Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysis

Glaucoma is a leading cause of irreversible visual field degradation, significantly impacting ocular health. Timely identification and diagnosis of this condition are critical to prevent vision loss. A range of diagnostic techniques is employed to achieve this, from traditional methods reliant on expert interpretation to modern, fully computerized diagnostic approaches. The integration of computerized systems designed for the early detection and classification of clinical indicators of glaucoma holds immense potential to enhance the accuracy of disease diagnosis. Pupillary Light Reflex (PLR) analysis emerges as a promising avenue for glaucoma screening, mainly due to its cost-effectiveness compared to exams such as Optical Coherence Tomography (OCT), Humphrey Field Analyzer (HFA), and fundoscopic examinations. The noninvasive nature of PLR testing obviates the need for disposable components and agents for pupil dilation. This facilitates multiple successive administrations of the test and enables the possibility of remote execution. This study aimed to improve the automated diagnosis of glaucoma using PLR data, conducting an extensive comparative analysis incorporating neural networks and machine learning techniques. It also compared the performance of different data processing methods, including filtering techniques, feature extraction, data balancing, feature selection, and their effects on classification. The findings offer insights and guidelines for future methodologies in glaucoma screening utilizing pupillary light response signals.

Low‐light visibility enhancement for improving visual surveillance in intelligent waterborne transportation systems

AbstractUnder low‐light imaging conditions, visual scenes captured by intelligent waterborne transportation systems often suffer from low‐intensity illumination and noise corruption. The visual quality degradation would lead to negative effects in maritime surveillance, e.g., vessel detection, positioning and tracking, etc. To restore the low‐light images, we develop an effective visibility enhancement method, which contains a coarse‐to‐fine framework of spatially‐smooth illumination estimation. In particular, the refined illumination is effectively generated by optimizing a novel structure‐preserving variational model on the coarse version, estimated through the Max‐RGB method. The proposed variational model has the capacity of suppressing the textural details while preserving the main structures in the refined illumination map. To further boost imaging performance, the refined illumination is adjusted through the Gamma correction to increase brightness in dark regions. We then estimate the refined reflection map by implementing the joint denoising and detail boosting strategies on the original reflection. In this work, the original reflection is yielded by dividing the input image using the refined illumination. We finally produce the enhanced image by multiplying the adjusted illumination and the refined reflection. Experiments on synthetic and realistic datasets illustrate that our method can achieve comparable results to the state‐of‐the‐art techniques under different imaging conditions.

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Abstract Traditional vision-based inertial odometry (VIO) suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units (IMUs). First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error (RMSE) by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

The Graded Incomplete Letters Test (GILT): a rapid test to detect cortical visual loss, with UK Biobank implementation

Impairments of object recognition are core features of neurodegenerative syndromes, in particular posterior cortical atrophy (PCA; the ‘visual-variant Alzheimer’s disease’). These impairments arise from damage to higher-level cortical visual regions and are often missed or misattributed to common ophthalmological conditions. Consequently, diagnosis can be delayed for years with considerable implications for patients. We report a new test for the rapid measurement of cortical visual loss – the Graded Incomplete Letters Test (GILT). The GILT is an optimised psychophysical variation of a test used to diagnose cortical visual impairment, which measures thresholds for recognising letters under levels of increasing visual degradation (decreasing "completeness") in a similar fashion to ophthalmic tests. The GILT was administered to UK Biobank participants (total n=2,359) and participants with neurodegenerative conditions characterised by initial cortical visual (PCA, n=18) or memory loss (typical Alzheimer’s disease, n=9). UK Biobank participants, including both typical adults and those with ophthalmological conditions, were able to recognise letters under low levels of completeness. In contrast, participants with PCA consistently made errors with only modest decreases in completeness. GILT sensitivity to PCA was 83.3% for participants reaching the 80% accuracy cut-off, increasing to 88.9% using alternative cut-offs (60% or 100% accuracy). Specificity values were consistently over 94% when compared to UK Biobank participants without or with documented visual conditions, regardless of accuracy cut-off. These first-release UK Biobank and clinical verification data suggest the GILT has utility in both rapidly detecting visual perceptual losses following posterior cortical damage and differentiating perceptual losses from common eye-related conditions.

Prediction of Visual Acuity in Pseudophakic Cataract Population Based on Residual Refraction

Purpose The purpose of the study was to design a simple, handy prediction for the effect of spherical and cylindrical refractive error on the visual acuity degradation at different distances and validate this model on a clinical dataset. Methods This study examined 70 eyes from 35 patients’ post-cataract surgery with aberration-free intraocular lenses. Biometric and corneal data were analysed, and subjective refraction and visual acuity were evaluated by two experienced optometrists. The study computed the spherical equivalent (SEQ), and defocus equivalent via vector addition (DEQ vec), as the sum of absolute values (DEQ abs). Predictive models were developed using univariate regression, with confidence intervals (BCa 95%) calculated through non-parametric bootstrapping (10,000 cycles). Results Various calculated equivalents included −0.44 D for spherical equivalent (SEQ), 0.70 D for defocus equivalent based on vector calculation (DEQ vec), and 0.89 D for defocus equivalent based on absolute values (DEQ abs). Uncorrected and corrected visual acuity averaged 0.07 logMAR and −0.04 logMAR, respectively. The absolute defocus equivalent (DEQ abs) exhibited the smallest confidence interval (BCa 95%) at 0.07. Conclusion The defocus equivalent based on the addition of absolute values (DEQ abs) emerged as the most practical predictor for the described applications. Notably, it offers the advantage of easy calculability through a simple equation: VA loss = DEQ abs ⋅ 0.23. In 95% of cases, this predicted loss would have an accuracy of ±0.03 lines.

Surgery for Primary Open Angle Glaucoma (POAG) By Trabeculectomy: Indications and Results in 52 Cases

Introduction: POAG filtering surgery arouses a lot of interest regarding its indications and results in melanoderma subjects south of the Sahara. The aim of this work was to contribute to improving the management of this disease in our socio-economic environment. Material and Methods: Retrospective longitudinal study carried out on the medical records of 47 patients and 52 eyes operated on for primary open-angle glaucoma by trabeculectomy from August 1, 2018, to August 31, 2020 in a private health facility, with postoperative follow-up of at least 1 year, for each case. Results: The patients were aged 10 to 70 years (mean 62.5 +/- 13.81 years), with a male predominance (51.92%). Those who had health coverage were 44 (93.61%). Patients referred by other ophthalmologists for better care were 26 (55.31%). The mean preoperative IOP was 22.67 mm Hg. The surgical indication was the failure of medical treatment marked by visual field degradation associated with uncontrolled IOP in 34 eyes (65.38%) and the patient's desire for 5 eyes (9.61%). And of the 52 operated eyes, 8 (15.39%) were under monotherapy, 30 under dual therapy (57.69%) and 14 (26.92%) under triple therapy. All eyes had undergone trabeculectomy, associated with the use of mitomycin C for the 52 eyes (100%). The postoperative IOP at 1 year of follow-up was between 10- and 15-mm Hg in 98.08% of cases with an average pressure drop of 8.67 mm Hg (38.24%). Conclusion: A common practice of trabeculectomy with a view to refining the indications and mastering both the technique and the management of complications could make it a reliable alternative in the management of POAG in certain patients who are non-compliant or unable to cope the financial cost of medical treatment for life, in our conditions

Towards Squeezing-Averse Virtual Try-On via Sequential Deformation

In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach. The tendency is empirically found that the textures of clothes are squeezed at the sleeve, as visualized in the upper row of Fig.1(a). A main reason for the issue arises from a gradient conflict between two popular losses, the Total Variation (TV) and adversarial losses. Specifically, the TV loss aims to disconnect boundaries between the sleeve and torso in a warped clothing mask, whereas the adversarial loss aims to combine between them. Such contrary objectives feedback the misaligned gradients to a cascaded appearance flow estimation, resulting in undesirable squeezing artifacts. To reduce this, we propose a Sequential Deformation (SD-VITON) that disentangles the appearance flow prediction layers into TV objective-dominant (TVOB) layers and a task-coexistence (TACO) layer. Specifically, we coarsely fit the clothes onto a human body via the TVOB layers, and then keep on refining via the TACO layer. In addition, the bottom row of Fig.1(a) shows a different type of squeezing artifacts around the waist. To address it, we further propose that we first warp the clothes into a tucked-out shirts style, and then partially erase the texture from the warped clothes without hurting the smoothness of the appearance flows. Experimental results show that our SD-VITON successfully resolves both types of artifacts and outperforms the baseline methods. Source code will be available at https://github.com/SHShim0513/SD-VITON.

Focus Stacking with High Fidelity and Superior Visual Effects

Focus stacking is a technique in computational photography, and it synthesizes a single all-in-focus image from different focal plane images. It is difficult for previous works to produce a high-quality all-in-focus image that meets two goals: high-fidelity to its source images and good visual effects without defects or abnormalities. This paper proposes a novel method based on optical imaging process analysis and modeling. Based on a foreground segmentation - diffusion elimination architecture, the foreground segmentation makes most of the areas in full-focus images heritage information from the source images to achieve high fidelity; diffusion elimination models the physical imaging process and is specially used to solve the transition region (TR) problem that is a long-term neglected issue and degrades visual effects of synthesized images. Based on extensive experiments on simulated dataset, existing realistic dataset and our proposed BetaFusion dataset, the results show that our proposed method can generate high-quality all-in-focus images by achieving two goals simultaneously, especially can successfully solve the TR problem and eliminate the visual effect degradation of synthesized images caused by the TR problem.

Carbonization temperature at 400°C and the behavior in the visual degradation of 15 wood species

Wood is an anisotropic material and its exposure to carbonization leads to reduced humidity and increased deformation due to the loss of its aesthetic and functional profile. Moisture may be an explanatory factor for the damage caused to the surface region of the coal. For this work, they were defined according to a basic density gradient, that is, involving species of low, medium and high densities, native and exotic, hardwood and conifers, as well as commercial and non-commercial uses. Among the species selected were: (Qualea sp.) Mandioqueira, (Aspidosperma sp.) Peroba, (Erisma uncinatum) Cedrinho, (Pinus elliotii) Pinus, (Eucalyptus.sp) Eucalipto, (Araucaria angustifolia) Pinheiro do Paraná, (Couratari sp.) Tauari, (Dipteryx sp.) Cumaru, (Tabebuia sp.) Ipê, (Peltogyne sp.) Roxinho, (Apuleia leiocarpa) Garapeira, (Goupia glabra) Cupiúba, (Astronium lecointei) Muiracatiara, (Hevea brasiliensis) Seringueira and (Schizolobium parahyba) Guapuruvu. At temperatures of 400 ºC, damage occurred in all species, with the influence of the temperature applied on the degradation suffered in the samples studied. The images produced can contribute to the understanding of charcoal as a material in its various temperature passages and thermal processes, in addition to analyzing the anatomy of charred samples, using the terms: ruptures and fissures as damage to charred wood as parameters.

Comprehensive analysis of aging mechanisms and design solutions for desert-resilient photovoltaic modules

This study aims to address the best practices and recommendations that contribute to the development of a tailored photovoltaic (PV) module design suited to desert conditions. To realize this customized design, a comprehensive analysis of in-situ aging assessment on five PV systems was undertaken. The initial phase of this assessment involved outdoor visual inspections, revealing visual material degradation modes such as snail trails, delamination, and discoloration. Subsequently, using infrared (IR) imaging and electroluminescence (EL) as powerful techniques, we pinpointed non-visual material issues such as hot spots, potential-induced degradation (PID) on p-type crystalline PV module, and micro-cracks emerging. The trends in the electrical parameters obtained from outdoor-measured I–V curves are analyzed to delve deeper into the performance decline of the PV modules. The findings revealed elevated degradation rates (Rd) values up to 2.7 %/y, largely attributed to the severe climate conditions at the investigation site. This poses significant implications for PV economic viability of the power plant and the payback time in such conditions. By focusing on the key material-related degradation modes that prevail in the exposed five PV systems employing a methodology that leverages Pareto analysis and integrates severity scores assessed by the National Renewable Energy Laboratory (NREL), we analyzed various mitigating solutions to address the dominant observed defects under the challenging desert conditions. Some recommendations on the constitutive parts of the module have been proposed to make them more resilient to the desert environment. These mitigating solutions include recommendations for enhancing the durability of the components of the module to better withstand the harsh desert environment.

Subjective and Objective Quality Assessment of Colonoscopy Videos.

Captured colonoscopy videos usually suffer from multiple real-world distortions, such as motion blur, low brightness, abnormal exposure, and object occlusion, which impede visual interpretation. However, existing works mainly investigate the impacts of synthesized distortions, which differ from real-world distortions greatly. This research aims to carry out an in-depth study for colonoscopy Video Quality Assessment (VQA). In this study, we advance this topic by establishing both subjective and objective solutions. Firstly, we collect 1,000 colonoscopy videos with typical visual quality degradation conditions in practice and construct a multi-attribute VQA database. The quality of each video is annotated by subjective experiments from five distortion attributes (i.e., temporal-spatial visibility, brightness, specular reflection, stability, and utility), as well as an overall perspective. Secondly, we propose a Distortion Attribute Reasoning Network (DARNet) for automatic VQA. DARNet includes two streams to extract features related to spatial and temporal distortions, respectively. It adaptively aggregates the attribute-related features through a multi-attribute association module to predict the quality score of each distortion attribute. Motivated by the observation that the rating behaviors for all attributes are different, a behavior guided reasoning module is further used to fuse the attribute-aware features, resulting in the overall quality. Experimental results on the constructed database show that our DARNet correlates well with subjective ratings and is superior nine state-of-the-art methods.

Frequency-aware network for low-light image enhancement

Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method.

A chromosome-level genome of electric catfish (Malapterurus electricus) provided new insights into order Siluriformes evolution.

The online version contains supplementary material available at 10.1007/s42995-023-00197-8.

A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.