Eye-Tracking Database for a Set of Standard Video Sequences

Hadizadeh, Hadi; Enriquez, Mario; Bajić, Ivan V.

doi:10.1109/tip.2011.2165292

Cited by 94 publications

(63 citation statements)

References 8 publications

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“…We calculate both the mean PSNR and mean EWPSNR values of all the reconstructed NK frames at various sampling rates. To compute the EWPSNR values, we exploit the first viewing eye-tracking data provided in [15]. Table 1 and Table 2 depict both the mean PSNR and mean EWPSNR results of all the considered DCVS codecs with a short GOP (i.e.,GOP=2) and a long GOP (i.e.,GOP=8), respectively.…”

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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“…The results in [28] showed that gaze locations in the first and second viewings can differ notably, however they remain relatively close to each other when there is a single dominant salient region in the scene (for example, the face in the Foreman sequence.) As a result, it is reasonable to expect that good saliency models will produce high scores for those frames where the first and second viewing data agree.…”

Section: B Eye-tracking Video Datasetsmentioning

confidence: 90%

“…1) The SFU Dataset: The SFU eye-tracking dataset [28] The first viewing is used as ground truth for evaluating the performance of saliency models, whereas the data from the second viewing is used to construct benchmark models, as described in Section II-C. The results in [28] showed that gaze locations in the first and second viewings can differ notably, however they remain relatively close to each other when there is a single dominant salient region in the scene (for example, the face in the Foreman sequence.)…”

Section: B Eye-tracking Video Datasetsmentioning

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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“…However, it can be very complex process for multiple scenes of a video and bitrate can be very high to encode multiple scenes of a video. In [18], HEVC Coding presented based on Fast Intra Prediction to reduce the computational complexity and bitrate while video encoding. in this paper, Screen content coding (SCC) used to provide low latency and fast transmission with HEVC technique.…”

Section: Video Encoding Issuesmentioning

confidence: 99%

“…This technique can used in field of medical, photography, satellite imaging, HDTV, stereoscopic video processing, face recognition and video coding or encoding to estimate saliency and compress high-definition videos largely. Our video compression provides fast computation for large training database such as SFU dataset [18] and HEVC video_database [19]. There are multiple factors, which makes HEVC architecture efficient and helps in enhancing the performance of the system.…”

Section: Video Encoding Based On Saliency Featuresmentioning

confidence: 99%

Efficient High Quality Video Assessment Using Salient Features

Rekha¹,

Kumar²

2017

IJIVP

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High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.

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Eye-Tracking Database for a Set of Standard Video Sequences

Cited by 94 publications

References 8 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

Compressed-domain visual saliency models: a comparative study

Efficient High Quality Video Assessment Using Salient Features

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