The recent advances in multimedia technology have significantly expanded the range of audio–visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion ...

• Summarizes immersion, interaction, and interconnection factors in video services.
• Reviews QoE assessment methods for video services focusing on key experience factors.
• Explores trends and challenges in QoE research for emerging ...

Given increasing demand for very large format contents and displays, spatial resolution changes have become an important part of video streaming. In particular, video downscaling is a key ingredient that streaming providers implement in their ...

• A network architecture to learn residuals prior to scaling and supports non-integer scaling factors, enhancing flexibility in video encoding workflows.
• The learned downsampling models was integrated with a realistic video encoding ...

A study on the quality evaluation of point clouds in the presence of coding distortions is presented. For that, four different point cloud coding solutions, notably the standardized MPEG codecs G-PCC and V-PCC, a deep learning-based coding ...

Recovering color images and videos from highly undersampled data is a fundamental and challenging task in face recognition and computer vision. By the multi-dimensional nature of color images and videos, in this paper, we propose a novel tensor ...

We propose a new Generative Adversarial Network for Compressed Video frame quality Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel Mul²Res block (with multiple levels of residual learning branches), an enhanced ...

• Presenting a novel block structure, Mul2Res which is the first use of a nested residual learning structure with various kernel sizes.
• Employing enhanced residual non-local blocks and enhanced convolutional block attention modules to ...

Hash learning is a hot topic since it can save storage space and perform fast retrieval. One of the most representative hashing methods is Supervised Discrete Hashing (SDH). However, there exist several problems in SDH. First, the potential of ...

• The jointly sparse feature extraction with orthogonal constraint is designed for hash learning.
• We design an iterative algorithm to optimize the model with closed-form solutions with less information loss.
• The proposed JSFH is ...

With the development of cloud computing, people usually outsource encrypted images for saving storage and protecting privacy. However, traditional image encryption methods not only hinder the availability of images such as similarity retrieval, ...

• Propose a cascaded information bottleneck of compression, security, availability.
• A deep image compression network is proposed to ensure compression performance.
• Design a feature division to find a subset that balance security and ...

Crowd counting aims to estimate the number of pedestrians in a scene. However, the problems of insufficient illumination and large-scale variation affect the accuracy of crowd counting. In this paper, a dilated high-resolution network (...

• RGB-T multi-modal crowd counting task is driven by a designed dilated highresolution network (DHRNet). The RGB and thermal modalities are aggregated in the ...

Assessing the quality of images is a challenging task. To achieve this goal, images must be evaluated by a pool of subjects following a well-defined protocol or an objective quality metric must be defined. In this work, an objective ...

• It is a no-reference approach based on a fully deep neural-network
• The saliency ...

Fine-grained image retrieval has gradually become a hot topic in computer vision , which aims to retrieve images with the same subcategories from general visual categories. Though fine-grained image retrieval has made a breakthrough ...

• We propose the GDF-Net framework to solve fine-grained image retrieval problems by mining correlations between discriminative features and constructing hash ...

Person re-identification is regarded as a retrieval task for searching the same person in different cameras, within which infrared-visible cross-modal re-identification (VI-ReID) is challenging because the inter-class distance is ...

• Exchange information among multiple classifiers.
• A collaborative learning ...

Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. ...

Display Omitted

• Faster R-CNN based signboard localization in densely populated developing cities.

In this paper, we propose a novel encryption algorithm using cascaded quaternion gyrator transforms that allows protecting multiple color images with efficiency at once. The originality is reflected in the integration of quaternion ...

• This paper designed a multiple color image encryption method.
• The introduced ...

The escalating demand for video content and streaming services has made it a predominant medium of exchanging information in the modern era. Videos are processed, compressed, and streamed over dynamic wireless channels having limited ...

The success of deep learning applications relies on a large number of labeled data. Active learning aims at identifying most informative unlabeled samples for labeling so as to achieve comparable performance with as few labeled data as ...

• A novel clustering based active learning method that can achieve better balance between uncertainty and diversity.

An entropy encoder for high-quality image compression in low-power, low-resources devices like wireless capsule endoscopy (WCE) or wireless camera sensor network (WCSN) is proposed. The proposed entropy encoder is optimized for ...

• DCT coefficients from different color planes have similar statistics.
• Usage of ...

Reversible data hiding in encrypted images (RDHEI) is an essential branch of image reversible data hiding. Over the past decade, many significant achievements have been made. However, for most RDHEI researches, the cloud service user (...

• This paper proposes an RDHEI method without any additional information transmission between the image owner and the data hider.

The study of video prediction models is believed to be a fundamental approach to representation learning for videos. While a plethora of generative models for predicting the future frame pixel values given the past few frames exist, ...

• A new database - IISc Predicted Videos Quality Assessment (PVQA) database containing 300 videos suffering from various distortions due to video prediction ...

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference ...

• We present a rank learning framework for UIE-IQA based on an elaborately designed self-supervision mechanism. It is also the first time that using deep learning approaches to address the UIE-IQA problem.
• We construct a dataset with ...

While storing invoice content as metadata to avoid paper document processing may be the future trend, almost all of daily issued invoices are still printed on paper or generated in digital formats such as PDFs. In this paper, we ...

• Invoice information extraction is an inevitable task in bulk document processing.