A no-Reference Stereoscopic Image Quality Assessment Network Based on Binocular Interaction and Fusion Mechanisms

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Objective.Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features.Approach.Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level.Main results.Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95%.Significance.The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network

Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain.

Stereo image quality assessment using deformed pixels and Sobel magnitude

The application of 3D technology is rapidly expanding, and stereoscopic imagery is typically used to display 3D data. However, compression, transmission, and other necessary processes may reduce the quality of these images. Stereo image quality assessment (SIQA) has gained more attention to guarantee that customers have a positive watching experience. In order to provide the highest level of experience, it is necessary to develop a quality evaluation mechanism for stereoscopic content that is both dependable and precise. A full-reference method for SIQA is presented in this paper. Compared to previous measures, this method gives users more freedom to use distorted pixel metrics and edge similarity. The binocular summation map is calculated by adding the left and right images for a stereo pair. Improved gradient similarity based distorted pixel measure (SGSDM) is used to calculate the quality of binocular summation. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score given by the database. The proposed method’s efficacy is demonstrated by experimental comparisons.