The intensity JND comes from Poisson neural noise: Implications for image coding

While the problems of image coding and audio coding have frequently been assumed to have similarities, specific sets of relationships have remained vague. One area where there should be a meaningful comparison is with central masking noise estimates, which define the codec's quantizer step size. In the past few years, progress has been made on this problem in the auditory domain (Allen and Neely, J. Acoust. Soc. Am., {\bf 102}, 1997, 3628-46; Allen, 1999, Wiley Encyclopedia of Electrical and Electronics Engineering, Vol. 17, p. 422-437, Ed. Webster, J.G., John Wiley \& Sons, Inc, NY). It is possible that some useful insights might now be obtained by comparing the auditory and visual cases. In the auditory case it has been shown, directly from psychophysical data, that below about 5 sones (a measure of loudness, a unit of psychological intensity), the loudness JND is proportional to the square root of the loudness \DL(\L) \propto \sqrt{\L(I)}. This is true for both wideband noise and tones, having a frequency of 250 Hz or greater. Allen and Neely interpret this to mean that the internal noise is Poisson, as would be expected from neural point process noise. It follows directly that the Ekman fraction (the relative loudness JND), decreases as one over the square root of the loudness, namely \DL/\L \propto 1/\sqrt{\L}. Above {\L} = 5 sones, the relative loudness JND \DL/\L \approx 0.03 (i.e., Ekman law). It would be very interesting to know if this same relationship holds for the visual case between brightness \B(I) and the brightness JND \DB(I). This might be tested by measuring both the brightness JND and the brightness as a function of intensity, and transforming the intensity JND into a brightness JND, namely \DB(I) = \B(I+ \DI) - \B(I) \approx \DI \frac{d\B}{dI}. If the Poisson nature of the loudness relation (below 5 sones) is a general result of central neural noise, as is anticipated, then one would expect that it would also hold in vision, namely that \DB(\B) \propto \sqrt{\B(I)}. %The history of this problem is fascinating, starting with Weber and Fechner. It is well documented that the exponent in the S.S. Stevens' power law is the same for loudness and brightness (Stevens, 1961) \nocite{Stevens61a} (i.e., both brightness \B(I) and loudness \L(I) are proportional to $I^{0.3}$). Furthermore, the brightness JND data are more like Riesz's near miss data than recent 2AFC studies of JND measures \cite{Hecht34,Gescheider97}

Auditory Processing in Children with Specific Language Impairments: Are there Deficits in Frequency Discrimination, Temporal Auditory Processing or General Auditory Processing?

Background/Aims: Specific language impairment (SLI) is believed to be associated with nonverbal auditory (NVA) deficits. It remains unclear, however, whether children with SLI show deficits in auditory time processing, time processing in general, frequency discrimination (FD), or NVA processing in general. Patients and Methods: Twenty-seven children (aged 8-11) with SLI and 27 control children (CG), matched for age and gender, were retrospectively compared with regard to their performance on five NVA skills in terms of just noticeable differences (JND) and time order judgments (TOJ). JND was used for FD, intensity discrimination, and gap detection, while TOJ was used for FD and clicks. Results: Children with SLI performed significantly worse than the CG only on the FD tasks (JND and TOJ). The other nonverbal tasks showed no significant intergroup differences. Additionally, moderate associations were found between the FD tasks and phonological skills, as well as between FD tasks and language scores. Conclusion: Children with SLI appear to have restricted FD skills compared to controls, but there was no evidence for a common NVA deficit or reduced temporal auditory abilities. Copyright (C) 2009 S. Karger AG, Base

SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.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

Color Image Watermarking using JND Sampling Technique

This paper presents a color image watermarking scheme using Just Noticeable Difference (JND) Sampling Technique in spatial domain. The nonlinear JND Sampling technique is based on physiological capabilities and limitations of human vision. The quantization levels have been computed using the technique for each of the basic colors R, G and B respectively for sampling color images. A watermark is scaled to half JND image and is added to the JND sampled image at known spatial position. For transmission of the image over a channel, the watermarked image has been represented using Reduced Biquaternion (RB) numbers. The original image and the watermark are retrieved using the proposed algorithms. The detection and retrieval techniques presented in this paper have been quantitatively benchmarked with a few contemporary algorithms using MSE and PSNR. The proposed algorithms outperform most of them. Keywords: Color image watermarking, JND sampling, Reduced Biquaternion, Retrieva

JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC

The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized. For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes. In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM. The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Visually lossless coding in HEVC : a high bit depth and 4:4:4 capable JND-based perceptual quantisation technique for HEVC

Due to the increasing prevalence of high bit depth and YCbCr 4:4:4 video data, it is desirable to develop a JND-based visually lossless coding technique which can account for high bit depth 4:4:4 data in addition to standard 8-bit precision chroma subsampled data. In this paper, we propose a Coding Block (CB)-level JND-based luma and chroma perceptual quantisation technique for HEVC named Pixel-PAQ. Pixel-PAQ exploits both luminance masking and chrominance masking to achieve JND-based visually lossless coding; the proposed method is compatible with high bit depth YCbCr 4:4:4 video data of any resolution. When applied to YCbCr 4:4:4 high bit depth video data, Pixel-PAQ can achieve vast bitrate reductions – of up to 75% (68.6% over four QP data points) – compared with a state-of-the-art luma-based JND method for HEVC named IDSQ. Moreover, the participants in the subjective evaluations confirm that visually lossless coding is successfully achieved by Pixel-PAQ (at a PSNR value of 28.04 dB in one test)

Just Noticeable Differences for Vehicle Rates of Closure

The goal for this research was to identify the just noticeable difference (JND) for vehicle rates of closure. In our attempt to identify the JND we used two traditional psychophysical methods. However, these procedures resulted aberrant relationships between rate of closure and percent correct. Both of the traditional procedures used a sequential presentation of a standard animation and a comparison animation. The final method used a change in the rate of closure within the animation. This method provided us with a JND of between 12.9 to 16.1 km/h (8 to 10 mph). Reasons for the aberrant findings using the traditional methods are discussed