Jaime Moreno

Towards No-Reference of Peak Signal to Noise Ratio

The aim of this work is to define a no-referenced perceptual image quality estimator applying the perceptual concepts of the Chromatic Induction Model The approach consists in comparing the received image, presumably degraded, against the perceptual versions (different distances) of this image degraded by means of a Model of Chromatic Induction, which uses some of the human visual system properties. Also we compare our model with an original estimator in image quality assessment, PSNR. Results are highly correlated with the ones obtained by PSNR for image (99.32% Lenna and 96.95% for image Baboon), but this proposal does not need an original image or a reference one in order to give an estimation of the quality of the degraded image.

pGBbBShift: Method for Introducing Perceptual Criteria to Region of Interest Coding

This work describes a perceptual method (pGBbBShift) for coding of Region of Interest (ROI) areas. It introduces perceptual criteria to the pGBbBShift method when bit planes of ROI and background areas are shifted. This additional feature is intended for balancing perceptual importance of some coefficients regardless their numerical importance. Perceptual criteria are applied using the CIWaM, which is a low-level computational model that reproduces color perception in the Human Visual System. Results show that there is no perceptual difference at ROI between the MaxShift method and pGBbBShift and, at the same time, perceptual quality of the entire image is improved when using pGBbBShift. Furthermore, when pGBbBShift method is applied to Hi-SET coder and it is compared against MaxShift method applied to both the JPEG2000 standard and the Hi-SET, the images coded by the combination pGBbBShift-Hi-SET get the best results when the overall perceptual image quality is estimated. The pGBbBShift method is a generalized algorithm that can be applied to other Wavelet based image compression algorithms such as JPEG2000, SPIHT or SPECK.

NRPSNR: No-Reference Peak Signal-to-Noise Ratio for JPEG2000

The aim of this work is to define a no-referenced perceptual image quality estimator applying the perceptual concepts of the Chromatic Induction Model. The approach consists in comparing the received image, presumably degraded, against the perceptual versions (different distances) of this image degraded by means of a Model of Chromatic Induction, which uses some of the human visual system properties. Also we compare our model with a original estimator in image quality assessment, PSNR. Results are highly correlated with the ones obtained by PSNR for image (99.32% Lenna and 96.95% for image Baboon), but this proposal does not need an original image or a reference one in order to give an estimation of the quality of the degraded image.

Image compression algorithm based on Hilbert Scanning of Embedded quadTrees: An introduction of the Hi-SET coder

In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder.

FabricVision: System of Error Detection in the Manufacture of Garments

A computer vision system is implemented to detect errors in the cutting stage within the manufacturing process of garments in the textile industry. It provides solution to errors within the process that cannot be easily detected by any employee, in addition to significantly increase the speed of quality review. In the textile industry as in many others, quality control is required in manufactured products and this has been carried out manually by means of visual inspection by employees over the years. For this reason, the objective of this project is to design a quality control system using computer vision to identify errors in the cutting stage within the garment manufacturing process to increase the productivity of textile processes by reducing costs.

Novel Algorithm for Stereoscopic Image Quality Assessment

Automatic or semi-automatic stereoscopic image quality assessment has arisen due to the recent diffusion of a new generation of stereoscopic technologies and content demand. Thereby, there is a growth in asking for algorithms of Stereoscopic Image Quality Metrics (SIQA). In this paper, we present a method for assessing the stereoscopic image quality, QUALITAS. QUALITAS is grounded on some human visual system features such as contrast sensitivity, effect of disparate image quality in left and right images, and distance perception, which do not depend on the images being tested. QUALITAS is defined in five stages. Instead of averaging individual qualities of the stereo-pair, QUALITAS introduces Contrast Band-Pass Filtering on a wavelet domain at both views, namely our algorithm perceptually weights left and right images depending on certain viewing conditions. This paper includes the comparison of 27 Metrics SIQA proposed by 16 authors, which summarizes the work made in this field in the recent five years, on image database LIVE 3D. Some algorithms can be combined with any 2D/Normal Image Quality Assessments (NIQA), giving as a result that QUALITAS was compared against 221 Metrics. QUALITAS obtained the best results in terms of overall performance of correlation coefficients. We conclude all metrics in SIQA-SET are simple modifications of NIQA, which take into account some extra characteristics from the disparity map (usually depth variances). Instead QUALITAS incorporates disparity masking in addition to divide 3D scenario in two parts: background and foreground planes. Moreover QUALITAS employs a contrast band-pass filtering, so dynamic parameters are considered as observational distance. It includes loss of correlation, luminance and contrast distortion. It takes into account the visual differences between left and right images, employing a penalization depending on their wavelet energy. Thus, the novelty of QUALITAS lies in combining some the best features of stereoscopic image quality assessments.

WHITE - DONKEY: Unmanned Aerial Vehicle for searching missing people

Searching for a missing person is not an easy task to accomplish,so over the years search methods have been developed, the problem is that the methods currently available have certain limitations and these limitations are reflected in time location. Time location in a person search is a very important factor that rescuers cannot afford to waste because the missing person is exposed to great dangers. In people search the vision system of the human being plays a very important role. The human visual system has the ability to detect and identify objects such as trees, walls, people among others besides to estimate the distance to them, this gives the human being the possibility of moving in their environment. With the development of artificial intelligence primarily to computer vision it is possible to model the human visual perception and generate computer software needed to simulate these capabilities. Using computer vision is expected to search for any missing person designing and implementing algorithms in order to an Unmanned Aerial Vehicle perform this task, also thanks to the speed of this is expected to reduce the time location. By using of a Unmanned Aerial Vehicle is not intended to replace the human being in the difficult task of searching and rescuing people but rather is intended to serve as a support tool in performing this difficult task.

BRIQA: Framework for the Blind and Referenced Visual Image Quality Assessment

Our proposal is to present a Blind and Referenced Image Quality Assessment or BRIQA. Thus, the main proposal of this paper is to propose an Interface, which contains not only a Full-Referenced Image Quality Assessment (IQA) but also a No- Referenced or Blind IQA applying perceptual concepts by means of Contrast Band-Pass Filtering (CBPF). Then, this proposal consists in contrast a degraded input image with the filtered versions of several distances by a CBPF, which computes some of the Human Visual System (HVS) variables. If BRIQA detects only one input, it performs a Blind Image Quality Assessment, on the contrary if BRIQA detects two inputs, it considers that a Referenced Image Quality Assessment will be computed. Thus, we first define a Full-Reference IQA and then a No-Reference IQA, which correlation is important when is contrasted with the psychophysical results performed by several observers. BRIQA weights the Peak Signal-to-Noise Ratio by using an algorithm that estimates some properties of the Human Visual System. Then, we compare BRIQA algorithm not only with the mainstream estimator in IQA, PSNR, but also state-of-the-art IQA algorithms, such as Structural SIMilarity (SSIM), Mean Structural SIMilarity (MSSIM), Visual Information Fidelity (VIF), etc. Our experiments show that the correlation of BRIQA correlated with PSNR is important, but this proposal does not need imperatively the reference image in order to estimate the quality of the recovered image.

Chapter 11 – ρGBbBShift: Method for introducing perceptual criteria to region of interest coding

This work describes perceptual generalized bitplane-by-bitplane shift (ρGBbBShift) a perceptual method for coding of region of interest (ROI) and it is based on Moreno et al. (2013) [1]. Then, this article introduces perceptual criteria to the GBbBShift method when bit planes of ROI and background areas are shifted. This additional feature is intended for balancing perceptual importance of some coefficients regardless of their numerical importance. Perceptual criteria are applied using a contrast band-pass filtering, which is a low-level computational model that reproduces color perception in the human visual system. Results show that there is no perceptual difference at ROI between the MaxShift method and ρGBbBShift and, at the same time, perceptual quality of the entire image is improved when using ρGBbBShift. Furthermore, when ρGBbBShift method is applied to Hi-SET coder and it is compared against MaxShift method applied to both the JPEG2000 standard and the Hi-SET, the images coded by the combination ρGBbBShift-Hi-SET get the best results when the overall perceptual image quality is estimated. The ρpGBbBShift method is a generalized algorithm that can be applied to other Wavelet-based image compression algorithms such as JPEG2000, SPIHT, or SPECK.

XSET: Image coder based on contrast band-pass filtering

Abstract Noise is fatal to image compression performance because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Some noise, in addition to some numerical redundancy, is removed during the quantization process, but in some circumstances the removed information is easily perceived by the observer, leading to annoying visual artifacts. Perceptual quantization reduces unperceivable details and thus improves both visual impression and transmission properties. In this work, we apply perceptual criteria in order to define a perceptual forward and inverse quantizer. It is based on the CBPF, a low-level computational model that reproduces color perception in the Human Visual System. Our approach consists in performing a local quantization of wavelet transform coefficients using some of the human visual system behavior properties. It is performed applying a local weight for every coefficient. The CBPF allows recovering these weights from the quantized data, which avoids the storing and transmission of these weights. We apply this perceptual quantizer to the H i -SET coder. The comparison between JPEG2000 coder and the combination of H i -SET with the proposed perceptual quantizer (XSET) is shown. The latter produces images with lower PSNR than the former, but they have the same or even better visual quality when measured with well-known image quality metrics such as MSSIM, UQI, or VIF, for instance. Hence, XSET obtain more compressed (i.e., lower bit-rate) images at the same perceptual image quality than JPEG2000.