Suguo Zhu

Bio: Suguo Zhu is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 5, co-authored 9 publications receiving 130 citations.

MLANs: Image Aesthetic Assessment via Multi-Layer Aggregation Networks

Abstract: Image aesthetic assessment aims at computationally evaluating the quality of images based on artistic perceptions. Although existing deep learning based approaches have obtained promising performance, they typically use the high-level features in the convolutional neural networks (CNNs) for aesthetic prediction. However, low-level and intermediate-level features are also highly correlated with image aesthetic. In this paper, we propose to use multi-level features from a CNN for learning effective image aesthetic assessment models. Specially, we extract features from multi-layers and then aggregate them for predicting a image aesthetic score. To evaluate its effectiveness, we build three multilayer aggregation networks (MLANs) based on different baseline networks, including MobileNet, VGG16, and Inception-v3, respectively. Experimental results show that aggregating multilayer features consistently and considerably achieved improved performance. Besides, MLANs show significant superiority over previous state-of-the-art in the aesthetic score prediction task.

KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment

Abstract: Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models ( $512\times 384$ ). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.

End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network

Abstract: The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly use recognition-oriented CNN for quality prediction. However, the properties of IQA task is different from image recognition task. Image recognition should be sensitive to visual content and robust to distortion, while IQA should be sensitive to both distortion and visual content. In this paper, an IQA-oriented CNN method is developed for blind IQA (BIQA), which can efficiently represent the quality degradation. CNN is large-data driven, while the sizes of existing IQA databases are too small for CNN optimization. Thus, a large IQA dataset is firstly established, which includes more than one million distorted images (each image is assigned with a quality score as its substitute of Mean Opinion Score (MOS), abbreviated as pseudo-MOS). Next, inspired by the hierarchical perception mechanism (from local structure to global semantics) in human visual system, a novel IQA-orientated CNN method is designed, in which the hierarchical degradation is considered. Finally, by jointly optimizing the multilevel feature extraction, hierarchical degradation concatenation (HDC) and quality prediction in an end-to-end framework, the Cascaded CNN with HDC (named as CaHDC) is introduced. Experiments on the benchmark IQA databases demonstrate the superiority of CaHDC compared with existing BIQA methods. Meanwhile, the CaHDC (with about 0.73M parameters) is lightweight comparing to other CNN-based BIQA models, which can be easily realized in the microprocessing system. The dataset and source code of the proposed method are available at https://web.xidian.edu.cn/wjj/paper.html .

Effective Aesthetics Prediction With Multi-Level Spatially Pooled Features

Abstract: We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.