Nikolay N. Ponomarenko

Image database TID2013

This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.

Color image database for evaluation of image quality metrics

In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.

DCT based high quality image compression

DCT based image compression using blocks of size 32x32 is considered. An effective method of bit-plane coding of quantized DCT coefficients is proposed. Parameters of post-filtering for removing of blocking artifacts in decoded images are given. The efficiency of the proposed method for test images compression is analyzed. It is shown that the proposed method is able to provide the quality of decoding images higher than for JPEG2000 by up to 1.9 dB.

Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signal-dependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images.

Efficiency analysis of color image filtering

This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

An automatic approach to lossy compression of AVIRIS images

Lossy compression of AVIRIS hyperspectral images is considered. An automatic approach to selection of compression parameters depending on noise characteristics in component images is proposed. Several ways of performing lossy compression are discussed and compared. It is shown that in order to minimize distortions and provide a sufficient compression ratio it is reasonable to group the channels according to the evaluated noise variances in subband images and depending upon the sensor that produces sets of subband images. It is shown that for real life images the attained compression ratios can be of the order 8. ..25.

High-Quality DCT-Based Image Compression Using Partition Schemes

This letter presents an advanced discrete cosine transform (DCT)-based image compression method that combines advantages of several approaches. First, an image is divided into blocks of different sizes by a rate-distortion-based modified horizontal-vertical partition scheme. Statistical redundancy of quantized DCT coefficients of each image block is reduced by a bit-plane dynamical arithmetical coding with a sophisticated context modeling. Finally, a post-filtering removes blocking artifacts in decompressed images. The proposed method provides significantly better compression than JPEG and other DCT-based techniques. Moreover, it outperforms JPEG2000 and other wavelet-based image coders

A New Color Image Database TID2013: Innovations and Results

A new database of distorted color images called TID2013 is designed and described. In opposite to its predecessor, TID2008, this database contains images with five levels of distortions instead of four used earlier and a larger number of distortion types (24 instead of 17). The need for these modifications is motivated and new types of distortions are briefly considered. Information on experiments already carried out in five countries with the purpose of obtaining mean opinion score (MOS) is presented. Preliminary results of these experiments are given and discussed. Several popular metrics are considered and Spearman rank order correlation coefficients between these metrics and MOS are presented and discussed. Analysis of the obtained results is performed and distortion types difficult for assessment by existing metrics are noted.

Lossy compression of images with additive noise

Lossy compression of noise-free and noisy images differs from each other. While in the first case image quality is decreasing with an increase of compression ratio, in the second case coding image quality evaluated with respect to a noise-free image can be improved for some range of compression ratios. This paper is devoted to the problem of lossy compression of noisy images that can take place, e.g., in compression of remote sensing data. The efficiency of several approaches to this problem is studied. Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio. Some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given. A novel DCT-based image compression method is briefly described and its performance is compared to JPEG and JPEG2000 with application to lossy noisy image coding.

Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics

In many modern applications, methods and algorithms used for image processing require a priori knowledge or estimates of noise type and its characteristics. Noise type and basic parameters can be sometimes known in advance or determined in an interactive manner. However, it occurs more and more often that they should be estimated in a blind manner. The results of noise-type blind determination can be false, and the estimates of noise parameters are characterized by certain accuracy. Such false decisions and estimation errors have an impact on performance of image-processing techniques that is based on the obtained information. We address some issues of such a negative influence. Possible structures of automatic procedures are presented and discussed for several typical applications of image processing as remote sensing data preprocessing and compression.