Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

The multiscale Hermite transform for local orientation analysis

Abstract: The efficient representation of local differential structure at various resolutions has been a matter of great interest for adaptive image processing and computer vision tasks. In this paper, we derive a multiscale model to represent natural images based on the scale-space representation: a model that has an inspiration in the human visual system. We first derive the one-dimensional case and then extend the results to two and three dimensions. The operators obtained for analysis and synthesis stages are derivatives of the Gaussian smoothing kernel, so that, for the two-dimensional case, we can represent them either in a rotated coordinate system or in terms of directional derivatives. The method to perform the rotation is efficient because it is implemented by means of the application of the so-called generalized binomial filters. Such a family of discrete sequences fulfills a number of properties that allows estimating the local orientation for several image structures. We also define the discrete counterpart in which the coordinate normalization of the continuous case is approximated as a subsampling of the discrete domain.

Image forensic analyses that elude the human visual system

Abstract: While historically we may have been overly trusting of photographs, in recent years there has been a backlash of sorts and the authenticity of photographs is now routinely questioned. Because these judgments are often made by eye, we wondered how reliable the human visual system is in detecting discrepancies that might arise from photo tampering. We show that the visual system is remarkably inept at detecting simple geometric inconsistencies in shadows, reflections, and perspective distortions. We also describe computational methods that can be applied to detect the inconsistencies that seem to elude the human visual system.

A novel objective no-reference metric for digital video quality assessment

Abstract: A novel objective no-reference metric is proposed for video quality assessment of digitally coded videos containing natural scenes. Taking account of the temporal dependency between adjacent images of the videos and characteristics of the human visual system, the spatial distortion of an image is predicted using the differences between the corresponding translational regions of high spatial complexity in two adjacent images, which are weighted according to temporal activities of the video. The overall video quality is measured by pooling the spatial distortions of all images in the video. Experiments using reconstructed video sequences indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment scores.

A metric for continuous quality evaluation of compressed video with severe distortions

Abstract: An objective quality metric that generates continuous estimates of perceived quality for low bit rate video is introduced. The metric is based on a multichannel model of the human visual system. The vision model is initially parameterized to threshold data and then further optimized using video frames containing severe distortions. The proposed metric also discards processing of the finest scales to reduce computational complexity, which also results in an improvement in the accuracy of prediction for the sequences under consideration. A temporal pooling method suited to modeling continuous time waveforms is also introduced. The metric is parameterized and evaluated using the results of a Single Stimulus Continuous Quality Evaluation test conducted for CIF video at rates from 100 to 800 kbps . The proposed metric exceeds the performance of a similar metric based on the Mean Squared Error.

Spatial-domain image hiding using image differencing

Abstract: A method to embed a secret image into a cover image is proposed. The method is based on the similarity among the grey values of consecutive image pixels as well as the human visual system's variation insensitivity from smooth to contrastive. A stego-image is produced by replacing the grey values of a differencing result obtained from the cover image with those of a differencing result obtained from the secret image. The process preserves the secret image with no loss and produces the stego-image with low degradation. Moreover, a pseudorandom mechanism is used to achieve cryptography. It is found from experiment that the peak values of signal-to-noise ratios of the method are high and that the resulting stego-images are imperceptible. Even when the size of the secret image is about a half of the cover image.