Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Authors: Feihong Shen
Chao Li
Yifeng Geng
Yongjian Deng
Hao Chen
Venue: NeurIPS 2024 poster (2024)
Keywords: Image Retargeting
content-aware seam-carving
adaptive repainting
Primary area: machine_vision

Abstract: Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as discontinuous pixels and inconsistent regenerated content. To address these issues, we propose a content-aware retargeting method called PruneRepaint. It incorporates semantic importance for each pixel to guide the identification of regions that need to be pruned or preserved in order to maintain key semantics. Additionally, we introduce an adaptive repainting module that selects image regions for repainting based on the distribution of pruned pixels and the proportion between foreground size and target aspect ratio, thus achieving local smoothness after pruning. By focusing on the content and structure of the foreground, our PruneRepaint approach adaptively avoids key content loss and deformation, while effectively mitigating artifacts with local repainting. We conduct experiments on the public RetargetMe benchmark and demonstrate through objective experimental results and subjective user studies that our method outperforms previous approaches in terms of preserving semantics and aesthetics, as well as better generalization across diverse aspect ratios. Codes will be available at
 https://github.com/fhshen2022/PruneRepaint.

Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Feihong Shen
Chao Li 111cho wwl.lcllllbbia iiaiaalibbaainn.... com
feihongshennseuu edu.
edu. cn
Yifeng Geng"
Yongjian Deng
Hao Chen
cangyu. wwwp:raggggaaaababaaaaaaaaaaaaa. inc.
yjdengebjut.cem edu. haochen303@seu. edu.
SSoutheast University
2 Alibaba Group
Beijing University of Technology
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
Abstract
Image retargeting is the task of adjusting the aspect ratio of images suit different display devices presentation environments. However, existing retargeting meth- ods often struggle to balance the preservation key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as discontinuous pixels and inconsistent regenerated content. To address these issues, propose content-aware retargeting method called PruneRepaint. incorporates semantic importance for each pixel to guide the identification of regions that need to be pruned preserved in order maintain key semantics. Additionally, we introduce an adaptive repainting module that selects image regions for repainting based on the distribution of pruned pixels and the proportion between foreground size and target aspect ratio, thus achieving local smoothness after pruning. By focusing on the content and structure of the foreground, our PruneRepaint approach adaptively avoids key content loss and deformation, while effectively mitigating artifacts with local repainting. We duct experiments on the public RetargetMe benchmark and demonstrate through objective experimental results and subjective user studies that our method outper- forms previous approaches in terms of preserving semantics and aesthetics, as well better generalization across diverse aspect ratios. Codes will be available https//ghhhhh..oom////////22////
1 Introduction
With the popularity of multi-screen and multi-aspect environments, people's demands for the adapt- ability and aesthetics of images across different devices are increasing. Consequently, image retar- geting 335 20 99, which aims to adjust the aspect ratio to fit various display devices or presentation environments while preserving the key content and maintaining the quality of the images, has distinctive applications yet is understudied.
The core challenge of image retargeting lies in simultancously 1) preserving the main information and 2) avoiding artifacts such as deformation and distortion on key objects. Intuitive solutions for this task include scaling and cropping. As shown in Figure (b), scaling entirely preserves all contents but results in severe deformation, decreasing aesthetic appeal and image quality, making difficult
"Corresponiing author (eteeem333@ssuued..cmmm
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(b) Scale
(c) Crop
(d) Seam-carving
(c) Ingan
ii) Ours
Original image
Deformation
Object loss
Distortion
Inconsistency
Expectation
Figure I: An example show bad cases such as deformation, content loss, discontinuity in lines,
inconsistent results and a good case.
to recognize the figures. On the contrary, crop-based methods [38 29 introduce no artifacts but often results in the loss of key semantics (see Figure (c)).. To relieve these problems, following methods typically use pixel-shifting operators. Noticeable works include seam-carving 222), which to identify seams for deletion. Some other works [ [4] further integrate these traditional operators to enhance the generalization to different (d), without semantics guidance for crucial regions, this line of methods often leads to content loss or distortion important objects, as well as inconsistent pixels in the foreground.
calculates energy using manual operators 6
2
scenarios. However, as illustrated in Figure I
integrate deep semantic features to guide the deletion preservation of pixels in traditional pixel-shifting methods. However, these methods fail to differentiate the semantic significance within objects (e.g.. face is more important than hair in person), thus often leading object distortion when meets oversized objects. Moreover, these methods only focus on foreground regions, typically leading to discontinuous backgrounds and decreased aesthetic appeal. Towards better aesthetics and region consistency, other works 3,31 22 8 achieve image retargeting from a generative perspective with Generative Adversarial Networks (GANs) [0]. These methods often implicitly learn the semantic distribution of images to regenerate retargeted images. Due to the absence of explicit semantic prior and the weakness of GANs in capturing the global data distribution 23 these methods will generate all regions without selection, resulting in inconsistent generation of key objects (Figure (e)).
Regarding the power of deep learning tools, some methods 16
34
To tackle the issues mentioned above, present content-aware retargeting model that maintain the essential semantics, their appearance consistency, and aesthetics while being adaptable to any aspect ratio. To alleviate semantic loss, we introduce content-aware seam-carving (CSC), which incor- porates hierarchical semantic information induced from semantic/spatial saliency to differentiate the energy to perform scene-level (i.e., background and foreground) and object-level (i.e., components in the foreground) pruning, thereby maximizing the preservation of key objects and their discriminative semantic elements. To mitigate artifacts introduced by pixel removal, we further propose an adaptive repainting (AR) method based on diffusion models, consisting of an Adaptive Repainting Region Determination (ARRD) module and an magge-guided Repainting (IR) module.
The two modules work together to adaptively repaint scenes with varying foreground sizes. The ARDD module is responsible for determining which regions of the image need to be repainted. does this by identifying abrupt pixels that have a high density of removed surrounding pixels. Then, considers the foreground size and desired ratio determine inpainting or outpainting. This approach ensures that important objects are preserved in the image even if they exceed the expected size, resulting flexible method that can handle images with any aspect ratio. Subsequently. IR refines the repainting process by using the original image as reference to restore and repaint these regions. Compared previous global generation methods [ 22 without maintaining foreground consistency with the original image, our approach selectively regenerates the abrupt pixels, preserving the foreground consistency and local smoothness effectively.
Our contributions can be summarized as follows:
1) We introduce content-aware image retargeting framework that is applicable to any aspect ratio. By incorporating content-awe seam-carving, our approach enables pixel pruning with hierarchical semantic differentiation.

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2) We propose an adaptive repainting method that utilizes sonee-oonditioee stable diffusion models. This method dynamically determines whether inpaint or outpaint based on different aspect ratios, leading to local smoothness and aesthetically pleasing outcomes.
3) Through extensive experiments involving various aspect ratios, our method demonstrates superior performance compared to other approaches in terms of both objective and subjective evaluations. It excels in preserving object completeness, coherence, and generalization.
2
Related Work
2.1 Image Retargeting
Existing image retargeting methods revolve around two main themes: preserving the main information and avoiding artifacts. Early image retargeting methods often fail to balance these two aspects. For instance, scaling attempts to maintain overall elements by uniformly removing pixels but struggles with significant changes in aspect ratios, resulting in severe deformation of key objects. Cropping- based methods [3 29 chooses the best window of target size from the original image, which preserves the structure but leads to the loss of crucial information outside the window. Seam-carving ① attempts to balance content completeness and quality by calculating energy maps to remove lower-energy seams. However, due to the lack of semantics, when the background is complex, this method usually result in the distortion foreground.
The rise of deep learning [5] has introduced semantic information to image retargeting, DeepIR [6
adopts pretrained VGG 32 to explicitly extract semantic information and retargets the image from a coarse semantic space to fine pixel space. SmartScale 44 utilizes existing object detection model to assist geam-carving However, these methods ignore the semantic differences within important regions, resulting in deformation within the oversized regions. In addition, neglecting the background can lead to discontinuities in background pixels, thereby affecting the aesthetic appeal of the image. For aesthetics and local smoothness, some methods adopt Generative Adversarial Networks (GANs) [10] to generate the retargeted results. InGAN (31] and SinGAN (30) divide the image into patches and learn the internal distribution of patches, destroying the overall semantics. To training GAN without partitioning the image, MRGAN [22) adopts multi-operator to generate a paired dataset, which is constrained by the handcraft, MCGAN 7 introduces mask to highlight importance areas. However, due to the limitations of implicit semantic expression, these methods preserve the global semantics but destroy the details, resulting in inconsistent appearance with the original image.
In contrast, our method is content- aware for selective pruning and adaptive repainting. It able to maintain the key semantics and appearance while ensuring local smoothness and aesthetics, and it demonstrates stronger generalization for different aspect ratios.
2.2 Diffusion Models for Image Generation
have become the mainstream models for generative tasks due to their powerful ability to model complex distributions. Stable Diffusion [26] is the first generative model based on latent diffusion models. The progressively denoising diffusion in latent space significantly enhances the efficiency, stability, realism, and controllability of image generation. Subsequently, various improvements (24) and variations [37 36) of stable diffusion models have been proposed. For instance, SDXL 24 adopts a larger backbone and finetunes using a complicated dataset with multiple aspect ratios to improve its versatility.
Nowadays, diffusion models []] 33 26
However, such text-to-image (T2I) models are hard to generate complex scenes and achieve more detailed control, as a significant amount of text control is labor-intensive and the T2I models struggle to accurately comprehend numerous and complex text prompts. To tackle this issue, other conditioning methods [77 [6] are proposed. The introduction of ControlNet [37] expands the applications of Stable Diffusion with different image-based conditional control, including depth images, mask images, canny images, etc. Ad-Atereer [36], newly proposed image-to-image (121) model, introduces image prompts to control condition with an decoupled cross-attention adapter branch, highly enhancing the controllability of the generative image.
3

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Adaptive Repainting
Target Rati
Adaptive Repainting Region Determination
Content-sware Searn-carving
Image guided Repuinting
Original Image
Sallency Map
guide
Targeted Image
Contert-aware Seam-carving
Adeptive Repeinting Region Deteminatiio
Target Ratio
entegg BY
Trigga
saliensy
energy
map
map
expanded mask map
element--wer
ID convolution along rows
convolutional kernel
Delete
Figure The overall architecture our proposed PruneRepaint. The input consists of RGB image and target ratio. The saliency map, obtained by saliency detection, is further introduced into content-aware caum-carving module for preliminary retargeting. The preliminary retargeted result is then processed by the adaptive repainting region determination module to identify the abrupt pixel regions that need to repainted. Utilizing the original image as guidance, the image is inpainted with the image-guis gaggeg repainting module, generating the final targeted image with target ratio.
In task, introduce image-guided local repainting into image retargeting, which enjoys the advantage more precise semantic preservation and more controllable local generation compared to global regeneration.
3
Method
3.1
Overall Architecture
The overall architecture is illustrated in Figure 2 Specifically, saliency detection model is adopted obtain the semantic saliency, which will further be combined with the initial energy map to guide the determination of pruning pixels, making the pruning content-aware. After that, an adaptive repainting region determination module is applied to identify the abrupt pixels and determine the repainting regions, and image-guided stable repainting module is further used to repaint them to output final retargeted image.
3.2 Content-aware Seam-carving
Seam-carving is typical pixel-shifting retargeting method, which calculates the energy of image and prioritizes deleting the seams with lower energy. For simplicity, we only discuss the scenario of deleting vertical seams in this section. The energy function in seam-carving is formulated as follows
① Energy(I(z, v)) = - [xx y)1 + [(x y)).
(1)
Dy
where [(x, y) denotes the pixel at position (x.y) in the image. Seam-carving carv-carving is often criticized for its lack of semantic information, which can lead to the distortion of key objects. To address this issue, we propose content-ane aware seam-carving (CSC), which incorporates semantic and spatial saliency priors. illustrated by the blue region Figure the energy function of semantic seam-carving is formulated as follows:
Energy (1x, v)) 0 [xr, v)1 + Fy
- (xx, w)1 + S(x, @
Fo W
where @ denotes element-wise multiplication, S(ar,) y) represents the saliency value at position (x.y), Zo is the x-coordinate of the saliency centroid achieved by averaging the x-coordinates of all salient

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pixels in the saliency map, W is the width of the image. The saliency map can be obtained through a pretrained salient object detection network [7]. By enhancing the energy important areas, the saliency prior (1z, y) prevents key objects from deformation. The spatial prior (1 |s- differentiates the importance within key regions, where the significance gradually decreases from the centroid towards the edges, thereby encouraging the model to prioritize seam removal from outer regions and retain key semantic elements for an object, ultimately avoiding distortion.
w further
For convenience, we follow [ to describe a vertical seam in an image as s = { = {(x(i), i) where sx-) is mapping subject to |æ(() - sri - 1)] S Given the energy function, we define the cost of seam as cos((s) = =Ei Energy(st). The seam to be deleted is selected by minimizing the cost:
= min, Σ Fnergy(s:)-
Using dynamic programming, we can efficiently find the seams with the least energy. We set a tolerable saliency loss ratio Ả to control the maximum loss of salient regions, which will be elaborated in (33.1 The maximun number of deleted seams is determined jointly by the saliency map and the tolerable saliency loss ratio A. Specifically, the quantity of seams to be deleted, which intersect the saliency map, must not exceed the product of the saliency width W, and the tolerable saliency loss ratio A, whether the image reaches the target ratio. We further get a binary mask S where 0 represents the low energy pixel to delete and 1 is the pixel to be preserved. The initial retargeting results can be obtained by performing dot product between the original image and the mask S and then concatenating the non-zero pixel regions.
3.3 Adaptive Repainting
The pixel-shift method inherently introduces pixel inconsistency, and bridging the resulting pixel gap poses a significant challenge. To address this issue, we introduce Adaptive Repainting (AR), a novel approach consisting of two primary components: the Adaptive Repainting Region Determination module (ARRD) and the Image-guides Repainting module (IR).
3.3.1 Adaptive Repainting Region Determination
The ARRD is designed to dynamically identify regions that require inpainting, which are characterized by inconsistencies among individual pixels. Additionally, ARRD determines the optimal repaint strategy (i.e., inpaint or outpaint) and corresponding regions by comparing the current ratio with the target ratio.
To generate the inpainting mask, we identify pixels with high number of deleted neighboring pixels in the content-aware seam carving (CSC) result as abrupt. As depicted by the green region in Figure 2 we employ a one me--imensionnal sliding window of length on the mask map S of seam- carving to calculate the mean value within the window. This can be formalized as a one-dimensional convolution: M = convld( (S,, K)/1, where convld 1 is one-dimension convolution operator, k is a one-dimensional convolution kernel of length I with all values equal to 1. We then binarize M into M using a threshold 7j. where indicates areas to be inpainted and denotes pixels to be preserved.
To generate the outpainting mask, we binarize the saliency map S into á using the mean value as the threshold. For each connected region in the saliency map, we compute its maximum width and then take the union of all these widths to obtain the saliency width W.. This can be formalized as:
W. sum (U mninnww1... wHw)
where w; is the i-th row of the binary saliency map $, Union(a, b) represents the union of two binary vectors a and b, and sum(a) denotes the sum of all elements in the vector a. Given the target ratio r, compare it with the target width We, which can be calculated as: W H r, where H is the height of original image. The final targeted width W, can be determined as:
W. (1 - X), We
W, * (1 - X) > We W, * (1 x) W,'
W
(5)
where \ is the tolerable saliency loss ratio, set to 0.3 in our experiment. The final height Hy is then calculated as Wy/r, /r,, and we can determine if the image needs expanding by comparing H, and H.
5

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formulate one reverse step in the Original image
Mask image
Y/
guide
IP-Adapter
ControlNet
V1-1
Figure 3: The architecture the image-guided repainting module.
The expanded height (Hy h) will be evenly distributed to the top and bottom of the image. The outpainting mask in this stage be merged into the inpainting mask M, hence the retargeting results can be obtained with unified repainting process with M.
3.3.2 Image-guided Repainting
to achieve repainting, a pretrained ControlNet [37]. replicated from the Stable Diffusion (SD) 26 Unet, is parallelly combined with the SD model. This ControlNet (specifically, the impaint version) serves to introduce features associated with visible regions of the image to be repainted. To harness the guidance provided by the original image, further introduce IP-Adapter [3). which consists of CLIP image encoder [25] and a lightweight adapter [12, to fuse image prompts with text prompts using decoupled cross-attention.
As shown in Figure
With the repainting mask M obtained in Section 3.3.1 diffusion process [19] to achieve repainting as follows:
Øt-1 M
1--1
known +1 M)
9i-1
unknoum
(6)
where Anoum sampled with the unmasked pixels in the given image M @ yo. while unknown is sampled from the model with the previous iteration y/:-
y
Experiments
4.1 Dataset and Evaluation Metrics
We evaluate the proposed method on the public image retargeting datasets, RetargetMe 27 which contains 80 images from various scenes. According to the common sizes of prevalent electronic devices, we set the target aspect ratio for image retargeting as 16:9, 1:1, 4:3 and 9:16.
The metrics for image retargeting have remained undetermined and existing evaluation metrics 18 [3] exhibit discrepancies with human perception, such as treating foreground and background equally. To intuitively evaluate the effectiveness of image retargeting methods, propose Saliency Discard Ratio (SDR) to assess the semantic preservation. The SDR can be calculated as follows:
Wori Wont
SDR
Wori
where Wori is the saliency width the original image defined in equation and Wont is the saliency width of the retargeted image.
User study metric. Given the subjective nature of retargeting results, we employ manual scoring as an additional evaluation method. Specifically, we invite 20 volunteers rate the results on scale

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from O to 3 across four aspects: content completeness, deformation, local smoothness, and aesthetics. These aspects are defined as follows: content completeness assesses whether key areas are cropped. deformation examines the degree of deformation within crucial areas, local smoothness evaluates the continuity of local regions the image, and aesthetics evaluates the overall harmony and aesthetic appeal of the visual composition. A higher score indicates better performance.
4.2 Implement Details
Our method is implemented using Pytorch RTX 3090. The length of the sliding window in Section 3.3.1 is set to = 25, and the threshold set 1 = 15. We utilize the VST model [17] for salient object detection in CSC. For the image-to-image repainting model in AR, employ composition $DI.5 ControlNet-Inpaining and Ad-Ateeeer [36].
4.3 Compare with Other Retargeting Methods
We quantitatively evaluate the performance of our proposed model by comparing it with three other prevalent image retargeting methods, namely scaling, cropping, seam-carving [1]. InGAN [3] and full repainting which repaints the whole image with SD1.5 and IP-Adapter (36], using the objective metric 'SDR' and four subjective metrics across different aspect ratios.
Table 1: Comparison of SDR values with other retargeting methods on the RetargetMe dataset with different aspect ratios. Lower values indicate better semantic completeness. The best results highlighted in bold.
Aspect Ratio
16/9
9/16
Scale
0.571
0.446
0.307
0.222
Crop
0.386
0.259
0.129
0.094
Seam-carving
0.490
0.367
0.242
0.161
InGAN
0,569
0.442
0,263
0,222
FR
0.524
0.423
0.294
0.214
Ours
0.151
0.074
0.031
0.006
Table presents the performance of various methods on objective metric. As shown in the table, our method achieves a significant reduction in the loss of salient regions, primarily due to the incorporation of saliency priors.
Table Subjective comparison with other retargeting methods in aspect ratio 16:9. indicates that larger are better. The best results are highlighted in bold.
Settings
Content completeness
Deformation
Local smoothness score
Aesthetic score t
Average
score f
score +
score
Scale
2.875
0.975
1.878
1.153
1,720
Crop
1.295
2.905
2,926
2.355
2,370
Seam-carving
2,829
0.973
1,000
1.038
1.461
InGAN
1.662
0.975
1.007
0.866
1.126
FR
1.327
1.812
1.702
1.535
1.594
Ours
2.345
2.757
2.689
2.538
2.582
Table presents the performance of different methods on four subjective evaluation metrics. As shown the table, scaling and cropping exhibit extremes, with scaling prioritizing content completeness and cropping prioritizing shape control. In contrast, our method receives high ratings across all four evaluation metrics. Notably, when compared to Table scaling exhibits significant discrepancies between subjective and objective metrics terms of key content preservation. We believe this is because the human eye has natural interpolation ability compared machines. Therefore, for scaling methods that uniformly delete pixels, subjective observers may not perceive strong content loss, even though objective metrics may indicate otherwise.

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Original image
Scale
Crop
Seam-Carving
InGAN
FR
Ours
Figure 4: Visual comparison to other retargeting methods on ratio 16:9.
Original
im-
Scale
Crop
Seam-Carving
InGAN
FR
Ours
age
Figure 5: Visual comparison to other retargeting methods ratio 9:16.
To qualitatively evaluate the performance of our proposed method, we visually compare our model with other retargeting methods, including scaling, cropping, seam-carving [ InGAN [3I] and full repainting (FR. which repaints the whole image with the guidance of original image using IP-Adapter (66) on different ratios. We conduct experiments with different ratios to provide an overall assessment of each method. Figure 4nd and Figure illustrrae the comparison of retargeting results with two extreme target ratios respectively. We can intuitively observe that most traditional methods produce inferior results due the lack of semantic information the oversized salient areas. They struggle to balance the trade-off between preserving key content and preventing significant object deformation. In contrast, our proposed method effectively preserves the essential content and structure of foreground objects while simultaneously maintaining harmonious and consistent background.
4.4 Ablation Study
In this section, we comprehensively conduct ablation experiments to verify the effectiveness each design in our proposed model on the popular aspect ratio 16:9.
Effectiveness of content-aware seam-carving. shown Table 3 content-aware seam-carving (denoted by *+CSC") significantly reduces the SDR, which means the salient objects are preserved much better. Besides, CSC can better preserve the strueture of key objects, as evidenced by Figure 6 Different from the original scam-carving, the addition of the CSC module results in minimal
8

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Table Ablation study of our retargeting meth- ods on ratio 16:9.
Table 4: Comparison of background repainting (BR) and our adaptive repainting (AR) on ratio 16:9.
Methods Seam-carving
SDR
0.490
Methods
SDR
0.190 +CSC
Background Repainting
0.190
+CSC+AR
0.151
Adaptive Repainting
0.151
Original image
Seam-carving
+CSC
+CSC+BR
+CSC+AR
Figure 6: Visualization to demonstrate the effectiveness of each component in our method. '+CSC' denotes content-aware seam-carving in section 3.2 *+CSC+BR' means adopt background repainting based on CSC, '+CSC+AR' means adaptive repainting in section 3.3 based on CSC.
deformation for the car. Also, the house with complex patterns maintains its basic structure and avoids significant global deformation.
Effectiveness of adaptive repainting. The comparison between '+CSC' and *CSC+AR" in Table 3 shows consistent improvement by the adaptive repainting module. As shown in Figure 6 AR adaptively identifies areas with abrupt pixels for repainting and adjusts the mask according to the target aspect ratio, leading to enhanced results.
Comparison between background repainting and adaptive repainting. To further validate the advantages of our proposed adaptive repainting method, we introduce Background Repainting (BR) strategy for comparison. BR identifies the background based on saliency maps as the region for regeneration. Table demonstrates the advantages of our AR method preserving salient regions, which is supported by Figure Specifically, BR is unable to address discontinuities in foreground pixels (see the car and the building Figure (). and the retargeting results are constrained by the ratio (the structure of car in the first row of Figure 6 due to extreme ratio). In contrast, our AR can identify all abrupt pixel regions and adapt well to extreme ratios.
4.5 Limitations
Constrained by the Stable Diffusion model, the inference speed of our method is relatively slow, aver- aging 7 seconds per image on the RetargetMe dataset (27]. This may limit its real-time applicability in certain scenarios. Moreover, the repainting region generated by ARRD is not complete enough, as ARRD searches the local pixel displacement area without global understanding. For instance, in comparing the 'Orignaal image' and *CSC+AR" images in the second row of Figure 6 several

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seams passing through the streetlight were removed, causing misalignment. AR only repaints the pixels around the deleted seams instead of the entire streetlight, resulting in streetlight that remains misaligned in the generated image.
5
Conclusion
Our paper introduces a new image retargeting model called PruneRepaint. This model is content- aware and adaptive, allowing to work with any target ratio. The content-aware seam-carving method protects important semantic regions, while the adaptive repainting module helps to maintain visual quality even after pixels are deleted. Through extensive experiments, we have demonstrated the effectiveness of our design and the advantages of using PruneRepaint for image retargeting.
Acknowledgments
The work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62162222002 102083; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210222; in part by the National Natural Science Foundation of China (NSFC) under Grant 62261 160576, Grant 62203024, and Grant 92167 102; and in part by the Research and Development Program of Beijing Municipal Education Commission under Grant KM200310005027.
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A Appendix supplemental material
A.1 Visualization on other ratios
In this section, we present the visual results of 6 retargeting methods at 4:3 and 1:1 aspect ratios. Observing the angler the first row Tiguresaand) demonstrates an outstanding ability to preserve key semantic details.
g it is evident that our PruneRepaint consistently
Original image
Scale
Crop
Seam-Carving
InGAN
FR
Ours
Figure 7: Visual comparison to other retargeting methods on ratio 4:3.
Original image
Scale
Crop
Seam-Carving
InGAN
FR
Ours
Figure 8: Visual comparison to other retargeting methods on ratio 1:1.
A.2 Additional Comparative Experiments
Considering the adverse impact of severe foreground distortions on aesthetic appeal, we introduce a spatial prior in Section t.2 to differentiate the degrees of importance within the foreground region, where pixel importance decays from the centroid outwards. As depicted in Figure 9 the space prior allows the foreground region to preferentially lose pixels on both sides within a certain range maintain the structure of important areas, which better conforms human visual perception.
Effectiveness of Space Prior.
Comparison of different generation methods. We compare the differences among various genera- tion methods, including full-image repainting, background repainting, and our adaptive repainting. The generated images are illustrated in Figure [0] Without the guidance of mask image, FR maintains coarse semantics but loses details. pixels (as evident in Figure 10 with the legs of the person and the pillars on both sides of the Taj Mahal). However, our can identify prominent areas of pixel discontinuity across the entire image for repainting.
struggles handle issue discontinuous foreground
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Original image
Seam-carving
+CSC w/o space prior +CSC w/ space prior
Figure Visualization to demonstrate the effectiveness of space prior.
Original image
+CSC
FR
BR
AR
Figure 10: Comparison of different generation methods. '+CSC' denotes the content-aware seam-
carving in section 3.2 'FR' means full-image repainting, 'BR' represents the background repainting and 'AR' is our adaptive repainting.
14

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Figure 11: An example to show different degrees of subjective metrics. The content completeness score, from left to right, could be 0223; the deformation score could be 3012: the local smoothness score could 3202; the aesthetic score could 2102. certain level subjectivity in scoring is permissible.
A.3 Detail of Subjective Experiments
Regarding the subjective experiment, provided brief training the volunteers, covering introduction to image retargeting, the differences between the four evaluation metrics, and the scoring range. Specifically, image retargeting is to "adjust the aspect ratio of an image fit various display devices or presentation environments while preserving the key content and maintaining the quality of the image". As for the metries, content completeness assesses whether key areas cropped, deformation examines the degree of deformation within crucial areas, local smoothness evaluates the continuity of local regions the image, and aesthetics evaluates the overall harmony and aesthetic appeal of the visual composition. Regarding the score, "0" indicates that the retargeted image severely compromises the attributes of the original image, while '3' denotes that the retargeted image retains the original attributes fine. Figure II provides visual example of the scoring among four subjective evaluation metries.
Subsequently, we distributed questionnaires containing the results of 80 images from the RetargetMe dataset [27] (reduced 79 images after removing sensitive content) under six different methods. The questionnaire collection time was 2 to hours, and we obtained 20 valid responses. We calculated the average scores for each evaluation metric. The specific description in the questionnaire is follows: The left side shows the original image, and the right side shows the image after being retargeted (higher scores indicate better
16:9 aspect ratio. Please rate each processed image from effectiveness). The compensation given to each volunteer was 500 CNY.
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22


Reviews:
[{"review_id": "evNTqv1c9v", "reviewer": "Reviewer_4WgC", "summary": "The work presents an addon using Diffusion models to Seam Carving to perform Content Aware resizing of images.", "questions": "Please see above.", "limitations": "Limitations not well discussed.", "rating": 6, "confidence": 5, "soundness": 1, "presentation": 1, "contribution": 1, "strengths": "+ None", "weaknesses": "- The work seems to be a rehash of Seam Carving, and some diffusion models were added to perform retargeting. There is no novelty in the method.\n\n- One page of the paper is direct equations from Seam Carving. How can it be a contribution?\n\n- The application is quite dated and has not made any significant through the methods proposed, which are logical but lack impact.\n\n- The writing is hazy, and there is barely any motivation about the approach and the application.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* **Weakness 1**: \n_No novelty in the method._  \n**Response**: \n    1) Seam-carving is a semantic-agnostic approach that often results in severe foreground loss and distortion (see Fig. 4, 5 and 7). In contrast, our proposed content-aware seam-carving (CSC) incorporates semantic awareness to preserve key objects, leveraging visual saliency cues and a careful integration strategy (see section 3.2). \n    2) Additionally, to address the inconsistent style and structure issues in previous global generation methods, we introduce adaptive repainting (AR). AR adaptively identifies discordant artifacts with a masking strategy and adjusts the mask to accommodate any aspect ratio. \n    3) The qualitative and quantitative comparisons in Sections 4.3 and 4.4 demonstrate the significant superiority of our method over previous approaches, highlighting the advantages of our designs.\n\n\n* **Weakness 2**: \n_One page of the paper is direct equations from Seam Carving._  \n**Response**: \nWe respectfully disagree that CSC is merely a rehash of seam-carving (SC). \nSC determines the importance of different structures in an image using low-level gradient information, which often leads to key foreground loss or deformation.\nTo address SC's lack of semantic awareness, we introduce high-level saliency priors alongside spatial priors (see lines 138 to 148), as presented in Eq. (2). \nAdditionally, a tolerable saliency loss ratio $\\lambda$ (see lines 154 to 161) is set to avoid excessive loss of the foreground in extreme ratios and to accommodate potential image expansion operations in AR.\nOur CSC successfully preserves key information. As demonstrated by the results in Tab. 3 and Figs. 6 and 7, our proposed CSC method achieves significant improvements over traditional SC. \n\n* **Weakness 3**: \n_The application is quite dated and has not made any significant through the methods proposed._  \n**Response**: \n    1) Regarding applications, the reviewer may refer to popular tools like Adobe Photoshop's \"Content-Aware Scaling\" and Instagram's \"Auto Crop\", which improve the work efficiency of media professionals and enhance the entertainment experience of the general public.\n    2) Our method significantly advances the two key aspects of retargeting - semantic completeness and local smoothness. This greatly enhances the user experience when deploying retargeting in real-world applications.\n\n* **Weakness 4**: \n_The writing is hazy, and there is barely any motivation about the approach and the application._  \n**Response**: \nOur motivation is to address two challenging and key issues in image retargeting tasks: preserving the main information and avoiding artifacts, as clearly stated in line 25 of the manuscript. CSC and AR are proposed to solve them respectively.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p5DXgoB2U8", "reviewer": "Reviewer_oiR7", "summary": "This work contributes to a new image retargeting model named PrueRepaint, which is adaptive to work with any target ratio. \n\nThe authors first improve the traditional seam-carving method with saliency priors to achieve content-aware pruning and protect important semantic regions. \n\nAfter that, they introduce an adaptive repainting module using the diffusion model to maintain local smoothness after pruning. \n\nThe method is unique compared to the previous and also effective. The newly proposed metrics are reasonable. Experiments have demonstrated the effectiveness of the key designs and the large advantages over other methods.", "questions": "- The authors point out that ‘the repainting region generated by ARRD is not complete enough and contains certain distorted regions’ in the limitation section. Please give more explanations for this limitation.\n\n- The result of ‘+CSC’ in Table 3 and ‘background repainting’ in Table 4 hold the same score. This should not be a coincidence, please explain the reason behind it.\n\n- For the IP-adapter, it seems that the proposed method does not rely on text prompts to fulfill or improve the retargeting result. However, I am still interested in what text prompt is used in your design. Have you tried different text prompts such as “keep key semantics” and how about the results?  \n\n- The output ratio is set to 16:9, 1:1, 4:3, and 9:16. What are the reasons for selecting these ratios as the target output? In previous image retargeting works, I also notice some other ratios such as 2:3 and other extreme ratios. \n\n-The repainting choice that inpainting or outpainting is based on setting a hyperparameter and decided by comparing the target ratio and the foreground size. I expect an adaptive strategy for this determination. Can you provide some potential solutions for this problem?", "limitations": "The authors discussed the limitations adequately.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- I believe the view that the authors address the image retargeting task is novel and very important. This work takes a step towards a spatial-variable diffusion model. In contrast, most diffusion models focus on the spatial-fixed community. \n\n- The method is well-designed and reasonable. The proposed idea differs a lot from previous cropping-based or global generation methods. The authors provide a more reasonable and effective solution path.\n\n- The authors also introduce two reasonable evaluation metrics.\n\n- Ablation studies verify the effectiveness of each design of the proposed method. \n\n- The proposed method achieves fairly superior results compared to previous models.", "weaknesses": "- It will be more convincing to verify the superiority of the proposed method if more methods such as the ‘InGAN’ model in Figure 1 can be involved for comparison. The previous methods address this task from varying views, such as using cropping, scaling, seam-carving, and a generative model. This work seems to provide a new idea. So I expect a more comprehensive comparison to other solutions to show its advantages.  \n\n- Although the proposed method has achieved large improvement over previous works on avoiding artifacts, the results still hold some distorted regions. In my view, strengthing local correlations will be helpful to solve this problem. \n\n- The dataset contains limited samples. Although the authors try to output different ratios and the results show the advantages and better generalization over previous works, it will be much better to enhance the results and comparisons with more samples. So, I suggest the authors collect a new high-quality dataset with diverse scenarios for this task and test the model with a larger dataset for evaluation in the future.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* **Weakness 1**: \n_Lack of comparison with more retargeting methods._  \n**Response**: \nThanks for your advice. We have added experiments on InGAN as well as full-image repainting (FR) in Fig. R1, R2 and Tab. R1 in the PDF. Both the quantitative and qualitative comparisons show the large superiority of our method.\n\n* **Weakness 2**:\n_Suggestions to solve the distorted regions with local correlations._  \n **Response**: \nYes, thanks for your constructive suggestion. Although our method has achieved significant improvement on both preserving semantic completeness and avoiding local artifacts, it is indeed a promising solution to further improve local smoothness by strengthening local correlations. However, the spatial misalignment between the original and retargeted images makes this local consistency constraint difficult to be implemented. We will study this problem carefully in our future work.\n\n* **Weakness 3**: \n_Lack of a larger high-quality dataset._  \n**Response**:\nThanks for your advice, we are actually working on the retargeting dataset. We collect images with a wider variety  of objects, input ratios, foreground scales, and object layouts for comprehensive evaluation. \n\n * **Question 1**: \n_Explain the limitation of ARRD._  \n**Response**: \nThe reason is that ARRD searches the local pixel displacement area without global understanding. For example, in comparing the 'Original image' and '+CSC+AR' images in the second row of Figure 6, some seams passing through the streetlight were removed, causing misalignment. Ideally, the entire streetlight should be repainted, but AR only repaints the pixels near the deleted seams in the middle, resulting in a streetlight that remains misaligned in the generated image.\n\n* **Question 2**: \n_Explain why the results of '+CSC' in Table 3 and ‘background repainting’ in Table 4 are the same._   \n**Response**:\nThe BR and AR are all implemented on the base of CSC. As BR identifies the background based on the saliency map for repainting, it will not change the saliency regions, thus they hold the same score.\n\n* **Question 3**: \n_About text prompts._  \n**Response**: \n    * For the first question, we regard Image Retargeting as an image-to-image generation task. Moreover, designing specific prompts for each image is quite expensive thus we do not utilize additional text prompts to assist in the task.\nThe positive text prompts used in the experiment are the default 'best quality, high quality,' while the negative prompts are 'monochrome, lowres, bad anatomy, worst quality, low quality.' The weight of the image prompt is set to 1, meaning that the text prompts contribute minimally.\n    * For the second question, text prompts are supposed to be captions that describe an image. However, prompts such as 'keep key semantics, preserve the main structure' are typically not captions of images. As a result, they are meaningless in guiding the generation process. \n\n* **Question 4**: \n_The reasons for selecting the reported ratios._  \n**Response**:\nMost existing devices have aspect ratios ranging from 16:9 to 9:16. The models that handle extreme ratios tend to perform better on the more common ratios as well. Therefore, we only select 4:3 and 1:1 as the middle-ground aspect ratios to test.  Among them, 16:9 and 4:3 are common aspect ratios for computers and televisions, 1:1 is a preferred image and video size for social media (such as Instagram), and 9:16 is commonly seen on smartphone screens.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GAz8YrjszQ", "reviewer": "Reviewer_HZC2", "summary": "This paper proposes a new image retargeting framework that prunes background and repaints local connections. It improves the traditional seam-carving with semantic guidance to make the pruning content-aware, avoiding deformation and loss of important objects. Meanwhile, the authors introduce an adaptive repainting module using an image-conditioned diffusion model to selectively inpaint or outpaint local regions to achieve local smoothness. The authors also design two evaluation metrics, including the Saliency Discard Ratio (SDR) and a user study metric for evaluation. The proposed method shows superior performance over others and generalization to varying target ratios.", "questions": "In equation (5), what does W_s denote? Is it the same as W in equation (2), meaning the width of the input image?\n\nFor the proposed metric SDR, the authors use the width change of the saliency map to measure semantic completeness. How to implement this measurement? If my understanding is correct, is it to sum the width of all lines in the saliency map and output the maximum one as the width of the saliency map? Why do not use the metrics in salient object detection such as F-measure or use IOU to evaluate?\n\nIn Table 2, I think content completeness and aesthetic scores are more important than the other two scores as they measure the two key views for image targeting. So, I wonder whether there is a better strategy to obtain an overall score rather than just averaging them.", "limitations": "The authors explicitly discussed the limitations of this work.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Good motivation and key problems are clearly summarized.\n\nThe method is technically sound and novel. I am very interested in this task and like the proposed idea. It appears to be a pioneering work in the image retargeting community using diffusion models. \n\nImpressive results and a large improvement over previous works in preserving object completeness, and coherence, and it holds better generalization.", "weaknesses": "As pointed out by the authors, the inference speed has a large room to be improved. I suggest the authors adopt some accelerated diffusion models to improve it.\n\nThe authors argue that their method can work well on any ratio, such as 1:1 and 4:3, yet these visual results are not illustrated.\n\nMinor writing or grammar mistakes, such as missing the point in line 70, and missing “the” in the caption of Figure 3.\n\nHow does the text prompt work? I am interested in its contributions to the retargeting results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* **Weakness 1**: \n_Inference speed to be improved._  \n**Response**:\nThanks for your suggestion, we will further employ accelerated diffusion models to improve the inference speed. \n\n* **Weakness 2:**\n_Results on ratio 1:1 and 4:3 are not visualized._  \n**Response**:\nThanks for pointing out this issue, we present visualization results for only two extreme ratios to more clearly demonstrate the advantages of PrueRepaint as other ratios hold less difficulties. Limited by the page size, visualized results for the other two aspect ratios will further be included in the appendix. \n\n* **Weakness 3**:\n_Minor writing or grammar mistakes._    \n**Response**:\nThanks for your detailed review, we will carefully proof the manuscript.\n\n\n* **Weakness 4**:\n_About the text prompts._  \n**Response**:\nHere we regard Image Retargeting as an image-to-image generation task and do not utilize additional text prompts to assist in the task. The positive text prompts used in the experiment are the default 'best quality, high quality,' while the negative prompts are 'monochrome, lowres, bad anatomy, worst quality, low quality.' The weight of the image prompt is set to 1, meaning that the text prompts contribute minimally.\n\n* **Question 1**:\n_The meaning of $W_s$ in equation (5)._  \n**Response**:\n$W_s$ is different from the image width $W$. $W_s$ denotes the saliency width which is defined in Eq. (4). We will add the description on $W_s$ in Eq (2).\n\n* **Question 2**: \n_How to implement $W_s$ in metric SDR? Is it to sum the width of all lines in the saliency map and output the maximum one as the width of the saliency map? Why do not use the metrics in salient object detection such as F-measure or use IOU to evaluate?_  \n**Response**: \n    * For the first and second questions, I'm afraid there might be a slight misunderstanding. $W_s$\nsums the width of all lines in the saliency map and takes their union rather than the maximum. In the implementation, we use a list of zeros with length $W$ to store whether each column has salient pixels. If a column has a salient pixel, we set the corresponding entry to 1. Finally, summing the list gives the salient width.\n    * For the last question, both saliency detection metrics and IOU are only suitable for evaluating two images with completely identical resolutions. However, image retargeting changes the aspect ratio as well as resolutions of the original image. Therefore, we proposed a simple metric that provides a rough estimate of saliency preservation. Thank you for your inspiring question, we will explore more retargeting metrics in our future research.\n\n* **Question 3**: \n_The weights of different subjective metrics._  \n**Response**:\nWe agree with you that different people and tasks have different biases towards subjective metrics, thus specific weights can be tailored to specific needs. We take averages just to compare the overall performance in a more intuitive and concise way.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KrISCaowVP", "reviewer": "Reviewer_RHY7", "summary": "The paper presents an innovative method for image retargeting. It addresses two core challenges in image retargeting (preserving the main information and avoiding artifacts on key objects) simultaneously by carefully devising a content-aware seam-carving method and an adaptive repainting method respectively. Both quantitative and qualitative results demonstrate the advantages of the method over others.", "questions": "1.\tFrom Figure 6, in addition to addressing the drawback of BR, which is unable to handle discontinuities in foreground pixels, I also notice that the inclusion of AR can more faithfully restore the depth and proportion of the original image (such as the size relationship between the cylinder and the background building in architectural images). Can the authors briefly discuss the reasons behind this advantage?\n2.\tI am very interested in the performance on videos. Intuitively, the proposed method will work well on each frame. However, inter-frame smoothness will be a new problem for videos. Without explicit inter-frame consistency regularization, will the proposed model result in cross-frame mutation and jitter?  \n3.\tIn line 266, what is the detailed difference between BR and AR. Both of them regenerate background regions, but what are the reasons that make BR unable to address discontinuities in foreground pixels?\n4.\tFrom Table 1, I notice that the performance for different target ratios varies a lot. Please explain this difference. \n5.\tIn line 178, how to decide the threshold η?", "limitations": "Limitations and analyses are included by the authors.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Significant improvement over prior works on both preserving semantic completeness and maintaining the harmony of the retargeted contents. \n2. The paper is well-motivated and the method presented is well-structured. \n3. The proposed idea is exciting. The semantic-guided local repainting solution is very reasonable and inspirable for the following researchers on this topic. The two key designs are effective.  \n4. The paper is well organized and written, with clear explanations of the method and effective visual representations.", "weaknesses": "1. Detailed explanations for some key concepts are missing and adding them will further enhance the clarity of this paper, e.g., in Line 142: the process for computing x_0 should be presented. Is it the saliency center of the entire image or the saliency center of a row or column?\n2. It will be better for this paper to demonstrate its advantages if the authors include recent deep learning works related to image editing/generation for comparison, especially those global generation methods. As stated, the proposed method selectively regenerates the abrupt pixels and preserves the foreground consistency and local smoothness over previous global generation methods without maintaining foreground consistency with the original image. Although Figure 1 shows an example, more comparisons will be highly beneficial to verify such advantages.\n3. The authors perform subjective evaluation by designing a User Study Metric. However, it lacks explanations with corresponding visual samples to show the complementarity among the four different evaluation metrics in Table 2. For example, the authors should include some samples with different degrees of deformation or distortion for comparison and explanation. \n4. The ARRD module in Figure 2 is not very clear to show the determination process for inpainting or outpainting. Expanding the mask map seems an optional choice and only works for some special cases when the foreground regions are large and direct cropping will lose too much object contents. However, in Figure 2, it appears always required for any input and ratio.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* **Weakness 1**: \n_Some detailed concepts are missed, such as $x_0$ in line 142._  \n**Response**:\nThanks for pointing it out. The center coordinate $x_0$ represents the center of the entire image. The height coordinate $H_{x_0}$ can be calculated as the average of the heights $H_i$ of all the salient pixels, where $i$ ranges from 0 to $n$, the total number of salient pixels:  \n$$H_{x_0} = \\frac{\\sum_{i=0}^{n} H_i}{n}. $$\nThe width coordinate can be determined in a similar manner.\n\n* **Weakness 2**:\n_Lack of comparison with recent image edit/generation methods._  \n**Response**:\nThanks for your suggestion. The results compared with full-image repainting (FR) and InGAN are included in Tab. R1, Tab. R2, Fig. R1 and Fig. R2 in the PDF. Both qualitative and quantitative results demonstrate that our method significantly outperforms existing approaches by our proposed CSC to preserve the key information and AR to enhance the local smoothness.  \n\n* **Weakness 3**:\n_Lack of explanations with corresponding visual samples to show the complementarity among the four different evaluation metrics in Table 2._  \n**Response**:\nAn example illustrating different degrees of content loss, deformation, distortion and aesthetics is presented in Fig. R3 of the PDF. Although the overall deformations in the third and fourth images are quite noticeable, the third image is very smooth locally, so it belongs to deformation rather than distortion, whereas the fourth image has both deformation and distortion. Further examples and scoring criteria will be included in the appendix.\n\n* **Weakness 4**:\n_The ARRD module in Figure 2 is not very clear._  \n**Response**:\nThanks for pointing out this issue, expanding the mask map is an optional step that will only be implemented when the retargeted image cannot accommodate salient objects. We will revise the flowchart with a dashed box and a legend to avoid this confusion.\n\n* **Question 1**:\n_Why AR restore the depth and proportion of the original image better than BR._  \n**Response**:\nThe expansion operation of AR reduces the seams that should have been removed from the foreground, thereby minimizing pixel displacement and alleviating misalignment of foreground objects. This allows for better preservation of the relative positions and depths of foreground objects.\n\n* **Question 2**:\n_Performance on the video._  \n**Response**:\nAn example of video retargeting is presented in Fig. R4 in the PDF. As expected, the lack of inter-frame consistency in the PruneRepaint approach leads to inconsistencies in generating objects in the background area. \n\n* **Question 3**:\n_The detailed difference between BR and AR in line 266._      \n**Response**:\nTo achieve background harmony and foreground preservation, BR identifies and repaints the background areas based on the saliency map, which does not affect the foreground and therefore fails to repair discontinuities in the foreground pixels.\nIn contrast, AR is designed to achieve harmony across the entire image, which adaptively identifies areas where CSC removed more seams, thereby mitigating discontinuities in both the foreground and background.\n\n\n* **Question 4**: \n_Explain why the performance for different target ratios varies a lot._     \n**Response**:\nTypically, the larger the aspect ratio difference between the retargeted image and the original image, the greater the loss in image saliency. We analyzed the RetargetMe dataset and found that the average aspect ratio is 0.7405, which is closest to 9/16 (0.5625), indicating minimal saliency loss at this ratio. As the aspect ratio deviates further from the original ratio, the Saliency Discard Ratio (SDR) increases.\n\n* **Question 5**:\n_How to decide threshold $\\eta$ in line 172._  \n**Response**:\nThe threshold $\\eta$ is the mean value of the entire saliency map.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all for your efforts and are glad to achieve your high recognition of the work's innovation, suitability and importance for image retargeting, significant outperformance, as well as the written. We gratefully thank all the reviewers for their constructive remarks and useful suggestions, which have greatly helped us to improve the quality of our manuscript. 

We thank all the reviewers for their efforts. We are encouraged they found our idea to be sound, clear and effective (**RHY7, HZC2, oiR7**). We are glad they found our approach to be intuitive and holds superiority in model performance (**RHY7, HZC2, oiR7**). We are pleased reviewer **RHY7** recognizes the potential of our research and **HZC2** and **oiR7** raise future insights for us. We address reviewer comments below.

Meta-review: This paper appears was a unanimous accept by the reviewers. They all appreciated the novelty and improvement over baselines, found the paper well-motivated, and felt the paper is a good contribution to the field. The author rebuttal was reviewed with discussion between the authors and the reviewers.

Congratulations!

In revising your paper for the camera-ready, please include and comments and clarifications that were made or proposed during the rebuttal and discussion phase.
Decision: Accept (poster)