Adaptive Image Compression Using Saliency and KAZE Features
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Presented at International Conference on Signal Processing and Communications (SPCOM), 2016, IISc Bangalore, India
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Adaptive Image Compression Using Saliency and KAZE Features
- 1. Adaptive Image Compression Using Saliency and KAZE Features Authors: Siddharth Srivastava, Prerana Mukherjee, Dr. Brejesh Lall SPCOM 2016 Department of Electrical Engineering Indian Institute of Technology, Delhi
- 2. Overview • Introduction • Background • Proposed Methodology • Experimental Results and Discussions • Conclusion SPCOM 2016
- 3. Introduction • Primary Objectives – To achieve image compression based on importance of the contents in the image. – Utilize properties from local regions to identify region importance. SPCOM 2016
- 4. Introduction • Underlying Principles – Differed Importance: The human eye gives more importance to salient regions – Transition: The surrounding regions around salient objects make them distinguishable – Rejection: Perceptually less significant Pixels/Regions can be made further insignificant – Object Characterization • Well defined boundary • Distinctive appearance • Uniqueness SPCOM 2016
- 5. Introduction • Core Methodology – We adapt the quality factor in JPEG compression scheme for each block instead of a global quality factor – This adaptation of quality parameter is based on saliency map and strength of KAZE keypoints SPCOM 2016
- 6. Introduction • Key Contributions: – JPEG Compatible – First to introduce KAZE for image compression – Maintains better perceptual quality at high compression ratios SPCOM 2016
- 7. Saliency Map Generation Extraction of features from the Image Form Activation Maps based on those features Combining Maps for different features into one Background: Saliency Map SPCOM 2016
- 8. Background: Saliency Map Computation Aimed at segmenting objects Weighted Combination SPCOM 2016 P. Mukherjee, B. Lall, and A. Shah, “Saliency map based improved segmentation,” in Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015.
- 9. Background: KAZE • KAZE is a recent feature detection technique which exploits the non linear scale space to detect keypoints along edges and sharp discontinuities. • Non linear diffusion filtering allows KAZE to achieve less blurring on edges as compared to Gaussian Blurring. SPCOM 2016
- 10. Saliency Map and KAZE Fig. 1: (a) Original Image (b) Saliency Map (c) KAZE keypoints SPCOM 2016 a) b) c)
- 11. Proposed Methodology Fig. 2: Architecture of (a) Compression and (b) Decompression SPCOM 2016
- 12. Adapting Quality Parameter (QP) with Saliency Response i: the 8x8 block in the image M. T. Khanna, K. Rai, S. Chaudhury, and B. Lall, “Perceptual depth preserving saliency based image compression,” in Proceedings of the 2nd International Conference on Perception and Machine Intelligence. ACM, 2015, pp. 218–223. SPCOM 2016
- 13. Adapting Quality Parameter (QP) with Keypoint Response i: the 8x8 block in the image SPCOM 2016
- 14. Algorithm I: Algorithm for Piecewise Adaptive QP – QSal = QJPEG * salBoost – MR = mean(image_response) – BS = blockStrength β1 * QSal if BS = 0 and salBoost < 1 QSal if BS = 0 and salBoost ≥ 1 or BS ≥ α * MR and BS < (1-α) * MR (1-α) * QSal if α * MR (1+α) * QSal if BS < (1-α)*MR and BS ≤ (1+α)*MR β2 * QSal otherwise boostedQP = *0 < α ≤ 0.5, β1 < 1 and β2 > 1 SPCOM 2016
- 15. Results SPCOM 2016 Fig. 3: (a) Plot showing the change in PSNR rate as the compression ratio changes (b) Plot between FSIMc with the varying compression ratio
- 16. Results SPCOM 2016 Fig. 4: (a) Original Image (b) Results after JPEG compression (c) Results after Adaptive Compression (Proposed Approach) a) b) c)
- 17. References • P. Mukherjee, B. Lall, and A. Shah, “Saliency map based improved segmentation,” in Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015. • M. T. Khanna, K. Rai, S. Chaudhury, and B. Lall, “Perceptual depth preserving saliency based image compression,” in Proceedings of the 2nd International Conference on Perception and Machine Intelligence. ACM, 2015, pp. 218–223. • F. Alcantarilla, A. Bartoli, and A. J. Davison, “Kaze features,” in Computer Vision–ECCV 2012. Springer, 2012, pp. 214–227. SPCOM 2016
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- 25. equation for building non linear scale space using AOS KAZE: Keypoint Detection SPCOM 2016
- 26. Comparison between gaussian blurring and nonlinear diffusion Non linear vs linear scale space SPCOM 2016
- 27. Feature detectionKAZE: Keypoint Detection SPCOM 2016
- 28. Scharr edge filter The Scharr operator is the most common technique with two kernels used to estimate the two dimensional second derivatives horizontally and vertically. The operator for the two direction is given by the following formula: KAZE: Keypoint Detection SPCOM 2016
- 29. Feature descriptionKAZE: Keypoint Description SPCOM 2016
- 30. KAZE: Keypoint Description SPCOM 2016
- 31. Piecewise function for further adapting QP SPCOM 2016