Computational Analysis of Correlations between Image Aesthetic and Image Naturalness in the Relation with Image Quality

Le, Quyet Tien; Ladret, Patricia; Nguyen, Hieu V.; Caplier, Alice

doi:10.3390/jimaging8060166

Cited by 2 publications

(3 citation statements)

References 53 publications

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“…In contrast, to highlight their structural lines and shapes, users use vertical poses when photographing buildings. Le et al [17] analyzed the relationship between aesthetic image attributes and overall aesthetics, finding that lighting and color attributes (e.g., balance and saturation) are more closely related to the overall aesthetic in natural rural scenes. It is associated with the rich and vivid colors usually seen in natural landscapes.…”

Section: Image Aesthetic Attribute Selection Standardmentioning

confidence: 99%

“…For this purpose, we propose a light IAPS that enhances the aesthetics of images. The IAPS integrates professional photography knowledge and aesthetic features to process images based on their categories [15][16][17], with minimal parameter adjustments to achieve maximum enhancement effects. The significance and potential applications of this study are illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Zhao,

Shi,

Han

et al. 2024

Applied Sciences

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Image aesthetics processing (IAP) is used primarily to enhance the aesthetic quality of images. However, IAP faces several issues, including its failure to analyze the influence of visual scene information and the difficulty of deploying IAP capabilities to mobile devices. This study proposes an automatic IAP system (IAPS) for mobile devices that integrates machine learning and traditional image-processing methods. First, we employ an extremely computation-efficient deep learning model, ShuffleNet, designed for mobile devices as our scene recognition model. Then, to enable computational inferencing on resource-constrained edge devices, we use a modern mobile machine-learning library, TensorFlow Lite, to convert the model type to TFLite format. Subsequently, we adjust the image contrast and color saturation using group filtering, respectively. These methods enable us to achieve maximal aesthetic enhancement of images with minimal parameter adjustments. Finally, we use the InceptionResNet-v2 aesthetic evaluation model to rate the images. Even when employing the benchmark model with an accuracy of 70%, the score of the IAPS processing image is verified to be higher and more effective compared with a state-of-the-art smartphone’s beautification function. Additionally, an anonymous questionnaire survey with 100 participants is conducted, and the result shows that IAPS enhances the aesthetic appeal of images based on the public’s preferences.

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Section: Image Aesthetic Attribute Selection Standardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Zhao,

Shi,

Han

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Image quality assessment (IQA) is a branch of computer vision research that aims to give computers the same ability to judge image quality as humans. IQA is a crucial research area since it can be applied to image restoration and used to distinguish between distinct visual perception experiences [ 1 ]. These days, digital images captured by cameras on cell phones, professional imaging equipment, and remote sensing satellites are widely used [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

Han

Zhao

et al. 2022

Sensors

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To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model's understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.

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Computational Analysis of Correlations between Image Aesthetic and Image Naturalness in the Relation with Image Quality

Cited by 2 publications

References 53 publications

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

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