Full Reference Objective Quality Assessment for Reconstructed Background Images

Mapping Intimacies ◽

10.20944/preprints201805.0240.v1 ◽

2018 ◽

Author(s):

Aditee Shrotre ◽

Lina J. Karam

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Quality Index ◽

Perceived Quality ◽

Reference Image ◽

Statistical Measures ◽

Full Reference ◽

Assessment Measures ◽

Background Image

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

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A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps

Algorithms ◽

10.3390/a13120313 ◽

2020 ◽

Vol 13 (12) ◽

pp. 313

Author(s):

Domonkos Varga

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Feature Vector ◽

State Of The Art ◽

Prediction Performance ◽

Perceptual Quality ◽

Reference Image ◽

Full Reference

The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal—called ActMapFeat—is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.

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A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

Multimedia Tools and Applications ◽

10.1007/s11042-021-10573-0 ◽

2021 ◽

Author(s):

Baisakhi Sur Phadikar ◽

Amit Phadikar ◽

Subro S. Thakur

Keyword(s):

Image Quality ◽

Image Retrieval ◽

Quality Assessment ◽

Image Quality Assessment ◽

Comprehensive Assessment ◽

Content Based Image Retrieval ◽

Reference Image ◽

Full Reference

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DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽

10.3390/s20226457 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6457

Author(s):

Hayat Ullah ◽

Muhammad Irfan ◽

Kyungjin Han ◽

Jong Weon Lee

Keyword(s):

Deep Learning ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

State Of The Art ◽

Reference Image ◽

Primary Concern ◽

Panoramic Images ◽

Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

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