Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with Flicker Test and QUEST+
2023-09-14, Jenadeleh, Mohsen, Hamzaoui, Raouf, Reips, Ulf-Dietrich, Saupe, Dietmar
The concept of video-wise just noticeable difference (JND) was recently proposed to determine the lowest bitrate at which a source video can be compressed without perceptible quality loss with a given probability. This bitrate is usually obtained from an estimate of the satisfied used ratio (SUR) at each bitrate, respectively encoding quality parameter. The SUR is the probability that the distortion corresponding to this bitrate is not noticeable. Commonly, the SUR is computed experimentally by estimating the subjective JND threshold of each subject using binary search, fitting a distribution model to the collected data, and creating the complementary cumulative distribution function of the distribution. The subjective tests consist of paired comparisons between the source video and compressed versions. However, we show that this approach typically over- or underestimates the SUR. To address this shortcoming, we directly estimate the SUR function by considering the entire population as a collective observer. Our method randomly chooses the subject for each paired comparison and uses a state-of-the-art Bayesian adaptive psychometric method (QUEST+) to select the compressed video in the paired comparison. Our simulations show that this collective method yields more accurate SUR results with fewer comparisons. We also provide a subjective experiment to assess the JND and SUR for compressed video. In the paired comparisons, we apply a flicker test that compares a video that interleaves the source video and its compressed version with the source video. Analysis of the subjective data revealed that the flicker test provides on average higher sensitivity and precision in the assessment of the JND threshold than the usual test that compares compressed versions with the source video. Using crowdsourcing and the proposed approach, we build a JND dataset for 45 source video sequences that are encoded with both advanced video coding (AVC) and versatile video coding (VVC) at all available quantization parameters. Our dataset is available at http://database.mmsp-kn.de/flickervidset-database.html.
Subjective Assessment of Global Picture-Wise Just Noticeable Difference
2020-07, Lin, Hanhe, Jenadeleh, Mohsen, Chen, Guangan, Reips, Ulf-Dietrich, Hamzaoui, Raouf, Saupe, Dietmar
The picture-wise just noticeable difference (PJND) for a given image and a compression scheme is a statistical quantity giving the smallest distortion that a subject can perceive when the image is compressed with the compression scheme. The PJND is determined with subjective assessment tests for a sample of subjects. We introduce and apply two methods of adjustment where the subject interactively selects the distortion level at the PJND using either a slider or keystrokes. We compare the results and times required to those of the adaptive binary search type approach, in which image pairs with distortions that bracket the PJND are displayed and the difference in distortion levels is reduced until the PJND is identified. For the three methods, two images are compared using the flicker test in which the displayed images alternate at a frequency of 8 Hz. Unlike previous work, our goal is a global one, determining the PJND not only for the original pristine image but also for a sequence of compressed versions. Results for the MCL-JCI dataset show that the PJND measurements based on adjustment are comparable with those of the traditional approach using binary search, yet significantly faster. Moreover, we conducted a crowdsourcing study with side-byside comparisons and forced choice, which suggests that the flicker test is more sensitive than a side-by-side comparison.
Influence of channel fluctuations on optimal real-time scalable image transmission
2003, Stankovic, Vladimir, Hamzaoui, Raouf, Saupe, Dietmar
Joint source-channel coding systems using scalable source codes and forward error correction allow reliable transmission of multimedia data over noisy channels. The performance of such systems highly depends on the source-channel bit allocation strategy. Rate-based error protection schemes, which maximize the expected source rate are very attractive for real-time applications because the optimization can be done very quickly and is independent of the source. In real-world communication, channel conditions are varying in time. Thus, it is important to frequently update the error protection. For two state-of-the-art joint sourcechannel coding systems, we show that a channel mismatch can lead to a poor performance. We study theoretically and experimentally the dependency of a rate-based optimal protection on the channel statistics and provide an efficient strategy for adjusting the error protection when a channel mismatch occurs.
Model-based real-time progressive transmission of images over noisy channels
2003, Charfi, Youssef, Hamzaoui, Raouf, Saupe, Dietmar
Many unequal error protection algorithms used in image communication systems need the operational distortion-rate (D/R) curve of the source coder whose computation is timeconsuming. We study the use of parametric models instead of the true D/R curves for wavelet-based embedded image and video coders. We propose a Weibull model and show its superiority to the previous models for real-time applications. For unequal error protection over binary symmetric and packet erasure channels, the Weibull model yielded performance similar to the one obtained with the true D/R curve while satisfying the real-time constraint.
Relaxed forced choice improves performance of visual quality assessment methods
2023-06, Jenadeleh, Mohsen, Zagermann, Johannes, Reiterer, Harald, Reips, Ulf-Dietrich, Hamzaoui, Raouf, Saupe, Dietmar
In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these experiments is the two-alternative forced choice method. Two stimuli with the same content but differing visual quality are presented sequentially or side-by-side. Subjects are asked to select the one of better quality, and when uncertain, they are required to guess. The relaxed alternative forced choice format aims to reduce the cognitive load and the noise in the responses due to the guessing by providing a third response option, namely, "not sure". This work presents a large and comprehensive crowdsourcing experiment to compare these two response formats: the one with the ``not sure'' option and the one without it. To provide unambiguous ground truth for quality evaluation, subjects were shown pairs of images with differing numbers of dots and asked each time to choose the one with more dots. Our crowdsourcing study involved 254 participants and was conducted using a within-subject design. Each participant was asked to respond to 40 pair comparisons with and without the "not sure" response option and completed a questionnaire to evaluate their cognitive load for each testing condition. The experimental results show that the inclusion of the "not sure" response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth. We also tested for the equivalence of the models and found that they were different. The dataset is available at http://database.mmsp-kn.de/cogvqa-database.html.
SUR-Net : Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning
2019, Fan, Chunling, Lin, Hanhe, Hosu, Vlad, Zhang, Yun, Jiang, Qingshan, Hamzaoui, Raouf, Saupe, Dietmar
The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.
Bild und Videokompression
2003, Saupe, Dietmar, Hamzaoui, Raouf
Der Science Fiction Autor Arthur C. Clarke hat einmal festgestellt, dass eine sich erfolgreich entwickelnde Technologie schließlich nicht mehr von Magie unterscheidbar ist. Im Jahr 1921 gelang es Harry G. Bartho- lomew und Maynard D. McFarlane Photographien zu digitalisieren und erstmals über ein transatlantisches Telegraphiekabel zwischen London und New York hin und her zu schicken.
Large-scale crowdsourced subjective assessment of picturewise just noticeable difference
2022, Lin, Hanhe, Chen, Guangan, Jenadeleh, Mohsen, Hosu, Vlad, Reips, Ulf-Dietrich, Hamzaoui, Raouf, Saupe, Dietmar
The picturewise just noticeable difference (PJND) for a given image, compression scheme, and subject is the smallest distortion level that the subject can perceive when the image is compressed with this compression scheme. The PJND can be used to determine the compression level at which a given proportion of the population does not notice any distortion in the compressed image. To obtain accurate and diverse results, the PJND must be determined for a large number of subjects and images. This is particularly important when experimental PJND data are used to train deep learning models that can predict a probability distribution model of the PJND for a new image. To date, such subjective studies have been carried out in laboratory environments. However, the number of participants and images in all existing PJND studies is very small because of the challenges involved in setting up laboratory experiments. To address this limitation, we develop a framework to conduct PJND assessments via crowdsourcing. We use a new technique based on slider adjustment and a flicker test to determine the PJND. A pilot study demonstrated that our technique could decrease the study duration by 50% and double the perceptual sensitivity compared to the standard binary search approach that successively compares a test image side by side with its reference image. Our framework includes a robust and systematic scheme to ensure the reliability of the crowdsourced results. Using 1,008 source images and distorted versions obtained with JPEG and BPG compression, we apply our crowdsourcing framework to build the largest PJND dataset, KonJND-1k (Konstanz just noticeable difference 1k dataset). A total of 503 workers participated in the study, yielding 61,030 PJND samples that resulted in an average of 42 samples per source image. The KonJND-1k dataset is available at http://database.mmsp-kn.de/konjnd-1k-database.html.
Fast algorithm for optimal error protection of embedded wavelet codes
2003-11, Stankovic, Vladimir, Hamzaoui, Raouf, Saupe, Dietmar
Embedded wavelet codes are very sensitive to channel noise because a single bit error can lead to an irreversible loss of synchronization between the encoder and the decoder. Sherwood and Zeger protected a zero-tree based embedded wavelet code sent through a memoryless noisy channel by using cyclic redundancy detection codes (CRC) and channel correction codes. Chande and Farvardin proposed an optimal joint source-channel allocation strategy for such systems. We show how to accelerate their algorithm without quality loss. For grey scale test images of size 512 x 512, our speedup factors ranged from 1.2 to 6 for total bit rates between 0.25 and 1.0 bits per pixel. Moreover, by using turbo codes as channel codes, we obtained competitive peak-signal-tonoise ratio (PSNR) results.
Fast algorithm for rate-based optimal error protection of embedded codes
2003, Stankovic, Vladimir, Hamzaoui, Raouf, Saupe, Dietmar
Embedded image codes are very sensitive to channel noise because a single bit error can lead to an irreversible loss of synchronization between the encoder and the decoder. Sherwood and Zeger introduced a powerful system that protects an embedded wavelet image code with a concatenation of a cyclic redundancy check coder for error detection and a rate-compatible punctured convolutional coder for error correction. For such systems, Chande and Farvardin proposed an unequal error protection strategy that maximizes the expected number of correctly received source bits subject to a target transmission rate. Noting that an optimal strategy protects successive source blocks with the same channel code, we give an algorithm that accelerates the computation of the optimal strategy of Chande and Farvardin by finding an explicit formula for the number of occurences of a same channel code. Experimental results with two competitive channel coders and a binary symmetric channel showed that the speed-up factor over the approach of Chande and Farvardin ranged from 2.82 to 44.76 for transmission rates between 0.25 and 2 bits per pixel.