DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression

We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our data-driven methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning

Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

A bag of words description scheme for image quality assessment

Every day millions of images are obtained, processed, compressed, saved, transmitted and reproduced. All these operations can cause distortions that affect their quality. The quality of these images should be measured subjectively. However, that brings the disadvantage of achieving a considerable number of tests with individuals requested to provide a statistical analysis of an image’s perceptual quality. Several objective metrics have been developed, that try to model the human perception of quality. However, in most applications the representation of human quality perception given by these metrics is far from the desired representation. Therefore, this work proposes the usage of machine learning models that allow for a better approximation. In this work, definitions for image and quality are given and some of the difficulties of the study of image quality are mentioned. Moreover, three metrics are initially explained. One uses the image’s original quality has a reference (SSIM) while the other two are no reference (BRISQUE and QAC). A comparison is made, showing a large discrepancy of values between the two kinds of metrics. The database that is used for the tests is TID2013. This database was chosen due to its dimension and by the fact of considering a large number of distortions. A study of each type of distortion in this database is made. Furthermore, some concepts of machine learning are introduced along with algorithms relevant in the context of this dissertation, notably, K-means, KNN and SVM. Description aggregator algorithms like “bag of words” and “fisher-vectors” are also mentioned. This dissertation studies a new model that combines machine learning and a quality metric for quality estimation. This model is based on the division of images in cells, where a specific metric is computed. With this division, it is possible to obtain local quality descriptors that will be aggregated using “bag of words”. A SVM with an RBF kernel is trained and tested on the same database and the results of the model are evaluated using cross-validation. The results are analysed using Pearson, Spearman and Kendall correlations and the RMSE to evaluate the representation of the model when compared with the subjective results. The model improves the results of the metric that was used and shows a new path to apply machine learning for quality evaluation.No nosso dia-a-dia as imagens são obtidas, processadas, comprimidas, guardadas, transmitidas e reproduzidas. Em qualquer destas operações podem ocorrer distorções que prejudicam a sua qualidade. A qualidade destas imagens pode ser medida de forma subjectiva, o que tem a desvantagem de serem necessários vários testes, a um número considerável de indivíduos para ser feita uma análise estatística da qualidade perceptual de uma imagem. Foram desenvolvidas várias métricas objectivas, que de alguma forma tentam modelar a percepção humana de qualidade. Todavia, em muitas aplicações a representação de percepção de qualidade humana dada por estas métricas fica aquém do desejável, razão porque se propõe neste trabalho usar modelos de reconhecimento de padrões que permitam uma maior aproximação. Neste trabalho, são dadas definições para imagem e qualidade e algumas das dificuldades do estudo da qualidade de imagem são referidas. É referida a importância da qualidade de imagem como ramo de estudo, e são estudadas diversas métricas de qualidade. São explicadas três métricas, uma delas que usa a qualidade original como referência (SSIM) e duas métricas sem referência (BRISQUE e QAC). Uma comparação é feita entre elas, mostrando- – se uma grande discrepância de valores entre os dois tipos de métricas. Para os testes feitos é usada a base de dados TID2013, que é muitas vezes considerada para estudos de qualidade de métricas devido à sua dimensão e ao facto de considerar um grande número de distorções. Neste trabalho também se fez um estudo dos tipos de distorção incluidos nesta base de dados e como é que eles são simulados. São introduzidos também alguns conceitos teóricos de reconhecimento de padrões e alguns algoritmos relevantes no contexto da dissertação, são descritos como o K-means, KNN e as SVMs. Algoritmos de agregação de descritores como o “bag of words” e o “fisher-vectors” também são referidos. Esta dissertação adiciona métodos de reconhecimento de padrões a métricas objectivas de qua– lidade de imagem. Uma nova técnica é proposta, baseada na divisão de imagens em células, nas quais uma métrica será calculada. Esta divisão permite obter descritores locais de qualidade que serão agregados usando “bag of words”. Uma SVM com kernel RBF é treinada e testada na mesma base de dados e os resultados do modelo são mostrados usando cross-validation. Os resultados são analisados usando as correlações de Pearson, Spearman e Kendall e o RMSE que permitem avaliar a proximidade entre a métrica desenvolvida e os resultados subjectivos. Este modelo melhora os resultados obtidos com a métrica usada e demonstra uma nova forma de aplicar modelos de reconhecimento de padrões ao estudo de avaliação de qualidade