Saliency-Aware Video Compression

Hadizadeh, Hadi; Bajić, Ivan V.

doi:10.1109/tip.2013.2282897

Cited by 300 publications

(114 citation statements)

References 50 publications

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“…Meanwhile, they all adopt the SPL algorithm for the CS reconstruction of the NK frames. Both the conventional peak signal to noise ratio (PSNR) and the recently proposed eye-tracking weighted peak signal to noise ratio (EWPSNR) [14] are adopted as the measure metrics for the objective quality of the reconstructed videos. The EWPSNR metric has been proved to be more closely correlation with the HVS and frequently employed to evaluate the perceptual coding performance.…”

Section: Resultsmentioning

confidence: 99%

Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation

Zhang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARY Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.

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Section: Resultsmentioning

confidence: 99%

Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation

Zhang

et al. 2017

IEICE Trans. Inf. & Syst.

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“…During the last few years another related track emerged where the goal is to segment out salient objects [10]- [20], instead of predicting some sparse eye-fixations. Both research tracks produce saliency maps that are useful for tasks such as video surveillance [21], compression [22], image manipulation [23], automatic image cropping [24], foreground detection [25], and coding [26], to name a few. However, the output of salient object detector techniques is more useful, when compared to eye fixation predictions, for higher level computer vision and pattern recognition …”

Section: Introductionmentioning

confidence: 99%

Probabilistic saliency estimation

Aytekin

Iosifidis

Gabbouj

2018

Pattern Recognition

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Abstract-In this paper, we model the salient object detection problem under a probabilistic framework encoding the boundary connectivity saliency cue and smoothness constraints in an optimization problem. We show that this problem has a closed form global optimum which estimates the salient object. We further show that along with the probabilistic framework, the proposed method also enjoys a wide range of interpretations, i.e. graph cut, diffusion maps and one-class classification. With an analysis according to these interpretations, we also find that our proposed method provides approximations to the global optimum to another criterion that integrates local/global contrast and large area saliency cues. The proposed approach achieves mostly leading performance compared to the state-of-the-art algorithms over a large set of salient object detection datasets including around 17k images for several evaluation metrics. Furthermore, the computational complexity of the proposed method is favorable/comparable to many state-of-the-art techniques.

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“…Many models have been introduced based on physiological and psychophysical findings to imitate the HVS in order to predict human visual attention [39]. Visual saliency models find a large number of applications in image processing and computer vision, such as quality assessment [14], [22], [52], [58], [70], [82], compression [24], [26], [27], [34], [55], [81], retargeting [19], [60], segmentation [23], [68], object recognition [30], object tracking [64], abstraction [41], guiding visual attention [29], [65], and so on.…”

Section: Introductionmentioning

confidence: 99%

Compressed-domain visual saliency models: a comparative study

Khatoonabadi

Bajić

Shan

2016

Multimed Tools Appl

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Abstract-Computational modeling of visual saliency has become an important research problem in recent years, with applications in video quality estimation, video compression, object tracking, retargeting, summarization, and so on. While most visual saliency models for dynamic scenes operate on raw video, several models have been developed for use with compresseddomain information such as motion vectors and transform coefficients. This paper presents a comparative study of eleven such models as well as two high-performing pixel-domain saliency models on two eye-tracking datasets using several comparison metrics. The results indicate that highly accurate saliency estimation is possible based only on a partially decoded video bitstream. The strategies that have shown success in compressed-domain saliency modeling are highlighted, and certain challenges are identified as potential avenues for further improvement.

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Saliency-Aware Video Compression

Cited by 300 publications

References 50 publications

Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation

Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation

Probabilistic saliency estimation

Compressed-domain visual saliency models: a comparative study

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