A Comprehensive Review on Content-Aware Image Retargeting: From Classical to State-of-the-art Methods

Asheghi, Bahareh; Salehpour, Pedram; Khiavi, Abdolhamid Moallemi; Hashemzadeh, Mahdi

doi:10.1016/j.sigpro.2022.108496

Cited by 22 publications

(3 citation statements)

References 111 publications

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“…As the existing IQAs are found to be insufficient, a low-level and deep-level feature combination-based IQA has been discussed recently in [13] as a solution. Furthermore, retargeted image quality assessment (RIQA), an important branch of IQA for assessing the quality of content-aware image retargeting in multimedia applications for HVS, is still in the early stage, as addressed in [14]. As technological advancement is still in progress for this application, developing an appropriate IQA is essential.…”

Section: Content Specificationmentioning

confidence: 99%

Is the development of objective image quality assessment methods keeping pace with technological developments?

Rahman,

Bhuiyan

2024

Int. j. electr. comput. eng. syst. (Online)

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Visual data proliferation with technological innovations is progressively moving forward, bringing automation, digitization, and smartness to various aspects of daily lives. These visual data-dependent technologies need to be assessed during their development to ensure the quality of services. Consequently, when appropriate quality assessment metrics are unavailable, the advancement of image processing technologies is hindered. This preliminary work addresses the disparity between the rapid advancement of image processing-based algorithms and the lag in developing image quality assessment (IQA) methods while briefly discussing future prospects. The shortcomings in IQA development are discussed by broadly categorizing application areas into human visual system (HVS)-based and computer vision-based, while highlighting the trends and necessity of developing application-specific IQA methods. Despite the existence of several emerging IQAs, this preliminary communication indicates significant opportunities for objective IQA developments in both application areas to support technological advancements. Hence, this work can serve academic and industrial communities by providing guidance for expediting IQA developments.

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Section: Content Specificationmentioning

confidence: 99%

Is the development of objective image quality assessment methods keeping pace with technological developments?

Rahman,

Bhuiyan

2024

Int. j. electr. comput. eng. syst. (Online)

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“…The size of the displayed image should be adjusted according to its resolution and aspect ratio. In addition, adjusting the image size to meet the user's growing communication speed makes automatic image adjustment an important research field [1]. Traditional image scaling techniques, such as fixed-window cropping and equal-scale scaling, focus on geometric constraints, but do not care about the image content and the overall visual importance.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Image Resizing Technology Based on Composition Detection and Composition Rules

Wang

et al. 2023

Electronics

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A novel content-aware image resizing mechanism based on composition detection and composition rules is proposed to address the lack of esthetic perception in current content-aware resizing algorithms. A composition detection module is introduced for the detection of the composition of the input image types in the proposed algorithm. According to the classification results, the corresponding composition rules in computational esthetics are selected. Finally, the algorithm performs the operations of seam carving using the corresponding esthetic rules. The resized image not only protects the important content of the image, but also meets the composition rules to optimize the overall visual effect of the image. The simulation results show that the proposed algorithm achieves a better visual effect. Compared with the existing algorithms, the proposed algorithm not only effectively protects important image content, but also protects important structures and improves the overall beauty of the image.

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“…Many researchers have studied image retargeting methods about retaining saliency information. The present methods can be divided into two categories [1] [2]. One is the straightforward method such as uniform scaling (SCL) and fixed window cropping [3]; the other is based on copy and delete, and Seam Carving (SC) [4] [5][6] [7] is a typical example.…”

Section: Introductionmentioning

confidence: 99%

An image retargeting method based on saliency map optimization and seam carving

Liu

Xue

2022

International Symposium on Artificial Intelligence and Robotics 2022

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A good image retargeting method can retain the important information of an image while changing its size. Image retargeting has been widely used in multi-size device displays and software thumbnail images. The existing image retargeting methods have some defects when they are used to process important regions of a large area and linear elements in the image. In this paper, an improved Seam Carving method is developed through optimizing the saliency map determination and operation flow. The saliency map is determined by Canny edge detection with adaptive threshold, Hough transforms for detecting straight lines, Yolo neural network and flood fill for sensitive area detection, etc. With these methods, the expression of essential information in pictures is improved. In the algorithm running process, the SC algorithm based on average energy and the similar simulated annealing algorithm based on seam neighborhood penalty are used to improve the running process of Seam Carving. Finally, experimental results on the open-source data set RetargetMe indicate that the proposed method achieves better performance in comparison with Seam-carving (SC), Shift-map (SM), Scale-and-stretch (SNS), and Warping methods.

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A Comprehensive Review on Content-Aware Image Retargeting: From Classical to State-of-the-art Methods

Cited by 22 publications

References 111 publications

Is the development of objective image quality assessment methods keeping pace with technological developments?

Is the development of objective image quality assessment methods keeping pace with technological developments?

Content-Aware Image Resizing Technology Based on Composition Detection and Composition Rules

An image retargeting method based on saliency map optimization and seam carving

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