Abstract
Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions of the image so that aesthetically pleasing retargeting results can be generated. In this paper, firstly various energy map generation methods are analyzed based on the conventional seam carving technique. After objective image quality assessment of the retargeted image obtained from the conventional seam carving technique, it has been found that the percentage of similarity obtained from the gradient energy map generation method is 77.44% which is higher than the other methods. Further, to minimize the deformations on the parameters such as luminance, color, and structure the improved seam carving technique utilizes the gradient energy generation method to obtain the energy map of all kinds of input images during the retargeting operation. To check the efficiency of the improved retargeting technique the obtained results are compared with the conventional seam carving technique based on different properties of the objects present in the image. After objective image quality assessment based on SSIM, it is found that the improved seam carving technique produces 70% similarity between reference and retargeted images which is 10% higher than the conventional seam carving technique. Furthermore, the energy modification operation of the improved seam carving technique also plays its significant contribution to minimize the deformation on the defined parameters. After the subjective and objective image quality assessment, it is found that the high percentage similarity in the retargeted results justifies the efficiency of the improved seam carving technique.
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Avidan, S. and Shamir, A.: Seam carving for content-aware image resizing. In: Proceedings of ACM SIGGRAPH 2007 papers, pp. 10-es, (2007) doi: https://doi.org/10.1145/1275808.1276390
Shamir, A., Avidan, S.: Seam carving for media retargeting. Commun. ACM 52(1), 77–85 (2009). https://doi.org/10.1145/1435417.1435437
Garg, A., Negi, A.: Structure preservation in content-aware image retargeting using multi-operator. IET Image Proc. 14(13), 2965–2975 (2020). https://doi.org/10.1049/iet-ipr.2019.1032
Zhang, Y., Sun, Z., Jiang, P., Huang, Y., Peng, J.: Hybrid image retargeting using optimized seam carving and scaling. Multim. Tools Appl. 76(6), 8067–8085 (2017). https://doi.org/10.1007/s11042-016-3318-1
Fang, Y., Fang, Z., Yuan, F., et al.: Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Trans. Syst., Man, Cybern.: Syst. 47(11), 2956–2966 (2016). https://doi.org/10.1109/TSMC.2016.2557225
Tang, Z., Yao, J., Zhang, Q.: Multi-operator image retargeting in compressed domain by preserving aspect ratio of important contents. Multim. Tools Appl. 3, 1–22 (2021). https://doi.org/10.1007/s11042-021-11376-z
Abhayadev, M., Santha, T.: Multi-operator content aware image retargeting on natural images. J. Sci. Ind. Res. 78, 193–198 (2019)
Garg, A., Negi, A., Jindal, P.: Structure preservation of image using an efficient content-aware image retargeting technique. SIViP 15(1), 185–193 (2021). https://doi.org/10.1007/s11760-020-01736-x
Garg, A., Negi, A.: A survey on content aware image resizing methods. KSII Trans. Internet Inf. Syst. (TIIS). 14(7), 2997–3017 (2020). https://doi.org/10.3837/tiis.2020.07.015
Chang, C.H., Chuang, Y.Y.: A line-structure-preserving approach to image resizing. In: IEEE conference on computer vision and pattern recognition, pp. 1075–1082, 2012 doi: https://doi.org/10.1109/CVPR.2012.6247786
Han, R., Ke, Y., Du, L., Qin, F., Guo, J.: Exploring the location of object deleted by seam-carving. Expert Syst. Appl. 95, 162–171 (2018). https://doi.org/10.1016/j.eswa.2017.11.023
Patel, D., Raman, S.: Accelerated seam carving for image retargeting. IET Image Proc. 13(6), 885–895 (2019). https://doi.org/10.1049/iet-ipr.2018.5283
Kiess, J., Kopf, S., Guthier, B., Effelsberg, W.: Seam carving with improved edge preservation. multimedia on mobile devices. Int. Soc. Opt. Photon. 7542, 75420 (2010)
Yang, Y., Cheng, Z., Yu, H., et al.: MSE-Net: generative image inpainting with multi-scale encoder. Vis. Computer. 65, 1–13 (2021). https://doi.org/10.1007/s00371-021-02143-0
Simakov, D., Caspi, Y., Shechtman, E., Irani, M.: Summarizing visual data using bidirectional similarity. In: IEEE conference on computer vision and pattern recognition, pp. 1–8, 2008 doi: https://doi.org/10.1109/CVPR.2008.4587842
Bolduc, F., Lejeune, A., Magnenat-Thalmann, N.: Image synthesis and 3-D computer animation: a new approach for strategic analysis. Vis. Comput. 3(1), 51–56 (1987). https://doi.org/10.1007/BF02153650
Nie, Y., Zhang, Q., Wang, R., Xiao, C.: Video retargeting combining warping and summarizing optimization. Vis. Comput. 29(6), 785–794 (2013). https://doi.org/10.1007/s00371-013-0830-4
Su, Z., Luo, X., Artusi, A.: A novel image decomposition approach and its applications. Vis. Comput. 29(10), 1011–1023 (2013). https://doi.org/10.1007/s00371-012-0753-5
Banerjee, A., Das, N., Santosh, K.C.: Weber local descriptor for image analysis and recognition: a survey. Vis. Computer. 68, 1–23 (2020). https://doi.org/10.1007/s00371-020-02017-x
Hashemzadeh, M., Asheghi, B., Farajzadeh, N.: Content-aware image resizing: an improved and shadow-preserving seam carving method. Signal Process. 155, 233–246 (2019). https://doi.org/10.1016/j.sigpro.2018.09.037
Abhayadev, M., and T. Santha.: Efficient retargeting of shadow images using improved CRIST. In: International conference on intelligent computing and control (I2C2), pp. 1–5, 2017
Senturk, Z.K. and Akgun, D.: Seam carving based image retargeting: A survey. In: 1st international informatics and software engineering conference (UBMYK), pp. 1–6, 2019
Chen, L., Fu, G.: Structure-preserving image smoothing with semantic cues. Vis. Comput. 36(10), 2017–2027 (2020). https://doi.org/10.1007/s00371-020-01950-1
Lin, W., Zhang, F., Lian, R., et al.: Seam Carving Algorithm Based on Saliency. In: International Conference on Smart Vehicular Technology, Transactions, Communication and Applications, pp. 282–291, 2017 doi: https://doi.org/10.1007/978-3-319-70730-3_34
Patel, D., Shanmuganathan, S., Raman, S.: Adaptive multiple-pixel wide seam carving. National conference on communications (NCC), pp. 1–6, 2019 doi: https://doi.org/10.1109/NCC.2019.8732245
Xu, J., Kang, H., Chen, F.: Content-aware image resizing using quasi-conformal mapping. Vis. Comput. 34(3), 431–442 (2018). https://doi.org/10.1007/s00371-017-1350-4
Guo, Z. and Zhang, J.: Seam Carving Algorithm for Maintaining the Shape Structure of Significant Objects. In: 2nd International Conference on Artificial Intelligence and Engineering Application (AIEA), pp. 651–658, 2017 doi: https://doi.org/10.12783/dtcse/aiea2017/14995
Zhang, L., Li, K., Ou, Z., Wang, F.: Seam warping: a new approach for image retargeting for small displays. Soft. Comput. 21(2), 447–457 (2017). https://doi.org/10.1007/s00500-015-1795-1
Shafieyan, F., Karimi, N., Mirmahboub, B., et al.: Image retargeting using depth assisted saliency map. Signal Process.: Image Commun. 50, 34–43 (2017). https://doi.org/10.1016/j.image.2016.10.006
Solanki, P., Bhatnagar, C., Jalal, A.S., et al.: Content Aware Image Size Reduction Using Low Energy Maps for Reduced Distortion. In: Proceeding of International Conference on Computer Visual and Image Proceeding, pp. 467–474, 2017 doi: https://doi.org/10.1007/978-981-10-2104-6_42
Guo, Y., Liang, Y., Yu, M., et al.: An improved seam carving algorithm based on image blocking and optimized cumulative energy map. J. Elect. Info. Tech. 40(2), 331–337 (2018). https://doi.org/10.11999/JEIT170501
Alavi Gharahbagh, A., Yaghmaee, F.: Improved content aware image retargeting using strip partitioning. Int. J. Eng. 31(5), 684–692 (2018)
Patel, D., Nagar, R., Raman, S.: Reflection symmetry aware image retargeting. Pattern Recogn. Lett. 125, 179–186 (2019). https://doi.org/10.1016/j.patrec.2019.04.013
Arai, K.: Modified seam carving by changing resizing depending on the object size in time and space domains. Int. J. Adv. Comput. Sci. Appl. 10(9), 143–150 (2019)
Choi, B., Lee, M., Jung, S.W. and Lu, Y.: Distortion-aware Panoramic Image Resizing Using Seam Carving. In: 2021 International Conference on Electrical, Information, and Communication (ICEIC), pp. 1–2, 2021 doi: https://doi.org/10.1109/ICEIC51217.2021.9369775
Rubinstein, M., Shamir, A., Avidan, S.: Multi-operator media retargeting. ACM Trans. Gr. (TOG). 28(3), 1–11 (2009). https://doi.org/10.1145/1531326.1531329
Dong, W.M., Bao, G.B., Zhang, X.P., et al.: Fast multi-operator image resizing and evaluation. J. Comput. Sci. Technol. 27(1), 121–134 (2012). https://doi.org/10.1007/s11390-012-1211-6
Kiess, J., Guthier, B., Kopf, S., et al.: SeamCrop for image retargeting. Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI. 8304, 83040K (2012) doi: https://doi.org/10.1117/12.906386
Zhou, Y., Chen, Z., Li, W.: Weakly supervised reinforced multi-operator image retargeting. IEEE Trans. Circuits Syst. Video Technol. 31(1), 126–139 (2020). https://doi.org/10.1109/TCSVT.2020.2977943
Valdez-Balderas, D., Muraveynyk, O. and Smith, T.: Fast Hybrid Image Retargeting. In: 2021 IEEE International conference on image processing (ICIP), pp. 1849–1853, 2021 doi: https://doi.org/10.1109/ICIP42928.2021.9506584
Mei, Y., Guo, X., Sun, D., Pan, G. and Zhang, J.: Deep Supervised Image Retargeting. In: 2021 IEEE international conference on multimedia and expo (ICME), pp. 1–6, 2021 doi: https://doi.org/10.1109/ICME51207.2021.9428129
Patel, D., and Raman, S.: Object proposals-based significance map for image retargeting. in: proceedings of 2nd international conference on computer vision and image Processing, pp. 89–101, 2018 doi: https://doi.org/10.1007/978-981-10-7898-9_8
Tsai, Y.J., Lin, H.J., Li, Y.S.: A straight line preserving seam carving technique. Appl. Mech. Mater. 385, 1453–1456 (2013). https://doi.org/10.4028/www.scientific.net/AMM.385-386.1453
Conge, D.D., Kumar, M., Miller, R.L., et al.: Improved seam carving for image resizing. IEEE workshop on signal processing systems, pp. 345–349, 2010 doi: https://doi.org/10.1109/SIPS.2010.5624813
Kumar, M., Conger, D.D., Miller, R.L., et al.: A distortion-sensitive seam carving algorithm for content-aware image resizing. J. Signal Process. Syst. 65(2), 159–169 (2011). https://doi.org/10.1007/s11265-011-0613-y
Kim, H.K., Lee, K.W., Jung, J.Y., et al.: A content-aware image stitching algorithm for mobile multimedia devices. IEEE Trans. Cons. Elect. 57(4), 1875–1882 (2011). https://doi.org/10.1109/TCE.2011.6131166
Zhang, D., Yin, T., Yang, G., Xia, M., Li, L., Sun, X.: Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies. J. Vis. Commun. Image Represent. 48, 281–291 (2017). https://doi.org/10.1016/j.jvcir.2017.07.006
Song, E., Lee, M., Lee, S.: CarvingNet: content-guided seam carving using deep convolution neural network. IEEE Access. 7, 284–292 (2018). https://doi.org/10.1109/ACCESS.2018.2885347
Guo, T., Xu, X.: Salient object detection from low contrast images based on local contrast enhancing and non-local feature learning. Vis. Computer. 45, 1–13 (2020). https://doi.org/10.1007/s00371-020-01964-9
Koo, H.I., Kuk, J.G. and Cho, N.I.: Eliminating structure misalignments using robust matching and image editing based on seam carving. In: 2009 16th IEEE international conference on image processing (ICIP), pp. 209–212, 2009 doi: https://doi.org/10.1109/ICIP.2009.5414470
Vaquero, D., Turk, M., Pulli, K., Tico, M., Gelfand, N.: A survey of image retargeting techniques. Appl. Digital Image Process XXXIII. 7798, 779814 (2010). https://doi.org/10.1117/12.862419
Kiess, J., Kopf, S., Guthier, B., Effelsberg, W.: A survey on content-aware image and video retargeting. Acm Trans. Multim. Comput., Commun., Appl. (TOMM). 14(3), 1–28 (2018). https://doi.org/10.1145/3231598
Chen, Y., Pan, Y., Song, M., Wang, M.: Improved seam carving combining with 3D saliency for image retargeting. Neurocomputing 151, 645–653 (2015). https://doi.org/10.1016/j.neucom.2014.05.089
Frankovich, M., Wong, A.: Enhanced seam carving via integration of energy gradient functionals. IEEE Signal Process. Lett. 18(6), 375–378 (2011). https://doi.org/10.1109/LSP.2011.2140396
Lin, H., Hosu, V., and Saupe, D.: KADID-10k: A large-scale artificially distorted IQA database. In: 2019 Eleventh International Conference on Quality of Multimedia Experiment (QoMEX), pp. 1–3, 2019. doi: https://doi.org/10.1109/QoMEX.2019.8743252
Wang, Z., Zhang, W., Zhou, H.: Perception-guided multi-channel visual feature fusion for image retargeting. Signal Process.: Image Commun. 79, 63–70 (2019). https://doi.org/10.1016/j.image.2019.08.015
Fang, Y., Zeng, K., Wang, Z., Lin, W., Fang, Z., Lin, C.W.: Objective quality assessment for image retargeting based on structural similarity. IEEE J. Emerg. Selec. Topics Circuits Syst. 4(1), 95–105 (2014). https://doi.org/10.1109/JETCAS.2014.2298919
Chen, Y., Liu, L., Tao, J., et al.: The improved image inpainting algorithm via encoder and similarity constraint. Vis. Computer. 36, 1–15 (2020). https://doi.org/10.1007/s00371-020-01932-3
Xin, Z., Fu, S.: User-centric QoE model of visual perception for mobile videos. Vis. Computer 35(9), 1245–1254 (2019). https://doi.org/10.1007/s00371-018-1590-y
Senturk, Z.K., Akgun, D., Senturk, A.: A performance analysis for seam carving algorithm. Int. J. Adv. Stud. Computers, Sci. Eng. 3(12), 5–11 (2014)
Venkataramanan, A.K., Wu, C., Bovik, A.C., Katsavounidis, I., Shahid, Z.: A Hitchhiker’s guide to structural similarity. IEEE Access. 9, 28872–28896 (2021). https://doi.org/10.1109/ACCESS.2021.3056504
Wei, Y., Xu, M.: Detection of lane line based on Robert operator. J. Measure. Eng 9(3), 156–166 (2021). https://doi.org/10.21595/jme.2021.22023
Zhai, G., Min, X.: Perceptual image quality assessment: a survey. Sci. China Inf. Sci. 63(11), 211301 (2020). https://doi.org/10.1007/s11432-019-2757-1
Zhang, Y., Lai, Y.K., Zhang, F.L.: Stereoscopic image stitching with rectangular boundaries. Vis. Computer 35(6), 823–835 (2019). https://doi.org/10.1007/s00371-019-01694-7
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Garg, A., Singh, A.K. Analysis of seam carving technique: limitations, improvements and possible solutions. Vis Comput 39, 2683–2709 (2023). https://doi.org/10.1007/s00371-022-02486-2
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DOI: https://doi.org/10.1007/s00371-022-02486-2