Deep Neural Network for Blind Visual Quality Assessment of 4K Content

• URL: http://arxiv.org/abs/2206.04363v1
• Date: Thu, 9 Jun 2022 09:10:54 GMT
• Title: Deep Neural Network for Blind Visual Quality Assessment of 4K Content
• Authors: Wei Lu, Wei Sun, Xiongkuo Min, Wenhan Zhu, Quan Zhou, Jun He, Qiyuan Wang, Zicheng Zhang, Tao Wang, Guangtao Zhai
• Abstract summary: Existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents. We propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality. The proposed model is trained through the multi-task learning manner and we introduce an uncertainty principle to balance the losses of the classification and regression tasks.
• Score: 37.70643043547502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement of spatial resolution. However, existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents due to the expanded resolution and specific distortions. In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction through experiments. Then we extract different kinds of visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score for each patch respectively. The overall quality index is obtained through the average pooling of patch results. The proposed model is trained through the multi-task learning manner and we introduce an uncertainty principle to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

Related papers

• Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild [66.40314964321557]
  We propose a novel IQA method named RichIQA to explore the rich subjective rating information beyond MOS to predict image quality in the wild. RichIQA is characterized by two key novel designs: (1) a three-stage image quality prediction network which exploits the powerful feature representation capability of the Convolutional vision Transformer (CvT) and mimics the short-term and long-term memory mechanisms of human brain. RichIQA outperforms state-of-the-art competitors on multiple large-scale in the wild IQA databases with rich subjective rating labels.
  arXiv  Detail & Related papers  (2024-09-09T12:00:17Z)
• Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency [51.36674160287799]
  We design a multi-branch deep neural network (DNN) to assess the quality of UHD images from three perspectives. aesthetic features are extracted from low-resolution images downsampled from the UHD ones. Technical distortions are measured using a fragment image composed of mini-patches cropped from UHD images. The salient content of UHD images is detected and cropped to extract quality-aware features from the salient regions.
  arXiv  Detail & Related papers  (2024-09-01T15:26:11Z)
• Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy [23.61467796740852]
  No-reference (NR) VQA methods play a vital role in situations where obtaining reference videos is restricted or not feasible. As more streaming videos are being created in ultra-high definition (e.g., 4K) to enrich viewers' experiences, the current deep VQA methods face unacceptable computational costs. In this paper, we propose a highly efficient and novel NR 4K VQA technology.
  arXiv  Detail & Related papers  (2024-07-30T12:10:33Z)
• Q-Ground: Image Quality Grounding with Large Multi-modality Models [61.72022069880346]
  We introduce Q-Ground, the first framework aimed at tackling fine-scale visual quality grounding. Q-Ground combines large multi-modality models with detailed visual quality analysis. Central to our contribution is the introduction of the QGround-100K dataset.
  arXiv  Detail & Related papers  (2024-07-24T06:42:46Z)
• Dual-Branch Network for Portrait Image Quality Assessment [76.27716058987251]
  We introduce a dual-branch network for portrait image quality assessment (PIQA) We utilize two backbone networks (textiti.e., Swin Transformer-B) to extract the quality-aware features from the entire portrait image and the facial image cropped from it. We leverage LIQE, an image scene classification and quality assessment model, to capture the quality-aware and scene-specific features as the auxiliary features.
  arXiv  Detail & Related papers  (2024-05-14T12:43:43Z)
• Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment [7.291687946822539]
  A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations. We enable the learning of low-level quality features to distortion types by introducing a novel quality-aware contrastive loss. We design zero-shot quality predictions from both pathways in a completely blind setting.
  arXiv  Detail & Related papers  (2023-12-08T05:24:21Z)
• Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network [20.835800149919145]
  Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. There is a performance drop when assessing distortion images generated by generative adversarial network (GAN) with seemingly realistic texture. We propose an Attention-based Hybrid Image Quality Assessment Network (AHIQ) to deal with the challenge and get better performance on the GAN-based IQA task.
  arXiv  Detail & Related papers  (2022-04-22T03:59:18Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial Pooling [13.750624267664156]
  We propose an image quality model that attempts to mimic the attention mechanism of human visual system (HVS) by using a recurrent neural network (RNN) The experimental study, conducted by using images with different resolutions from two recently published image quality datasets, indicates that the quality prediction accuracy of the proposed method is competitive against benchmark models representing the state-of-the-art, and the proposed method also performs consistently on different resolution versions of the same dataset.
  arXiv  Detail & Related papers  (2021-06-02T03:31:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.