Shaping datasets: Optimal data selection for specific target distributions across dimensions

Vonikakis, Vassilios; Subramanian, Ramanathan

doi:10.1109/icip.2016.7533061

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…With respect to the sampling procedure, the method of Vonikakis et al [11] can ensure a uniform distribution for each attribute independently. However, we are also interested in sampling videos with joint distortions, having extreme values in several attributes simultaneously.…”

Section: Samplingmentioning

confidence: 99%

The Konstanz natural video database (KoNViD-1k)

Hosu

Hahn

Jenadeleh

et al. 2017

2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)

251

245

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Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small number of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be 'general purpose' requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 publicdomain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at 'in the wild' authentic distortions, depicting a wide variety of content.

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Section: Samplingmentioning

confidence: 99%

The Konstanz natural video database (KoNViD-1k)

Hosu

Hahn

Jenadeleh

et al. 2017

2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)

251

245

View full text Add to dashboard Cite

show abstract

“…download 750, 000 images from the Internet followed by automatic pre-screening to remove duplicate and nonphotographic images. Afterward, we sample 100, 000 images with marginal distributions nearly uniform with respect to image attributes, including bitrate, JPEG compression ratio, brightness, colorfulness, contrast, and sharpness [40]. Finally, we down-sample the images such that the long edge has 1, 024 pixels as a way of facilitating computational prediction.…”

Section: Experimental Setupsmentioning

confidence: 99%

Troubleshooting Blind Image Quality Models in the Wild

Wang

Chen

et al. 2021

Preprint

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Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "bestperforming" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.

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“…Before sampling from these videos, we crop these videos to 10 seconds and remove the audio parts. To enable the characteristics of sampled videos uniformly distributed in terms of these features, we adopt the sampling strategy introduced in [39]. In particular, the original videos are characterized with a set S,…”

Section: B Video Samplingmentioning

confidence: 99%

User-generated Video Quality Assessment: A Subjective and Objective Study

Li¹,

Meng²,

Zhang³

et al. 2020

Preprint

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Recently, we have observed an exponential increase of user-generated content (UGC) videos. The distinguished characteristic of UGC videos originates from the video production and delivery chain, as they are usually acquired and processed by nonprofessional users before uploading to the hosting platforms for sharing. As such, these videos usually undergo multiple distortion stages that may affect visual quality before ultimately being viewed. Inspired by the increasing consensus that the optimization of the video coding and processing shall be fully driven by the perceptual quality, in this paper, we propose to study the quality of the UGC videos from both objective and subjective perspectives. We first construct a UGC video quality assessment (VQA) database, aiming to provide useful guidance for the UGC video coding and processing in the hosting platform. The database contains source UGC videos uploaded to the platform and their transcoded versions that are ultimately enjoyed by endusers, along with their subjective scores. Furthermore, we develop an objective quality assessment algorithm that automatically evaluates the quality of the transcoded videos based on the corrupted reference, which is in accordance with the application scenarios of UGC video sharing in the hosting platforms. The information from the corrupted reference is well leveraged and the quality is predicted based on the inferred quality maps with deep neural networks (DNN). Experimental results show that the proposed method yields superior performance. Both subjective and objective evaluations of the UGC videos also shed lights on the design of perceptual UGC video coding.

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Shaping datasets: Optimal data selection for specific target distributions across dimensions

Cited by 15 publications

References 13 publications

The Konstanz natural video database (KoNViD-1k)

The Konstanz natural video database (KoNViD-1k)

Troubleshooting Blind Image Quality Models in the Wild

User-generated Video Quality Assessment: A Subjective and Objective Study

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