A review on document image analysis techniques directly in the compressed domain

The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data

Development and analysis of image and video coding and indexing method for medical examinations with applications in telemedicine

Orientadores: Wu Feng Chung, Huei Diana LeeTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: O surgimento da videoendoscopia digestiva alta e baixa e da telemedicina constituem importantes avanços tecnológicos para o diagnóstico e o treinamento em procedimentos de exames do trato gastrointestinal por métodos ópticos. Nesse cenário, a transmissão e o armazenamento de imagens e vídeos digitais demandam técnicas de compressão adequadas às características da aplicação. Além disso, com a formação de grandes bases de dados, a recuperação por conteúdo das imagens de modo efetivo é também necessária. Assim sendo, este trabalho propõe um método original de codificação, indexação e recuperação de imagens e vídeos. O método usa a decomposição em valores singulares como técnica algébrica de fatoração que permite a ordenação dos fatores constituintes por nível de importância na constituição da imagem. O método apresenta, também, uma análise estatística prévia de cores obtidas de amostras representativas, formando um mapa de probabilidade baseado na frequência de cores, e que resulta na representação compacta das mesmas. Por meio de experimentos realizados com 2.700 quadros de endoscopia digestiva alta, o método de codificação alcançou taxa de compressão média de 82,07%, com desvio padrão de 6,37%, e qualidade objetiva de 38,85 dB com desvio padrão de 1,06 dB. Em comparação com o padrão de codificação H.263, houve diferença estatisticamente significativa em termos da taxa de compressão e da qualidade objetiva (p ? 0,05). Para a recuperação, foram indexados 7.212 quadros e utilizadas 97 imagens de exemplo para a busca. Como resultado, a precisão média alcançada foi de 94,85% na configuração que obteve o melhor desempenho geral, tendo sido igual ou superior a outros métodos reportados na literatura. Por meio dos experimentos realizados conclui-se que o método original proposto foi eficaz em codificar e indexar automaticamente vídeos de exames médicos para aplicações de telemedicina.Abstract: The emergence of upper and lower gastrointestinal (GI) video endoscopy and telemedicine are essential technological advancements for the diagnosis and training related to examination procedures of the digestive tract using optical methods. In this context, transmission and storage of digital images and videos require compression techniques that are suitable for the application characteristics. Moreover, as the media databases grow massive, effective search and retrieval by image content become necessary. Thus, this work proposes an original method for coding, indexing and retrieving images and videos. The proposed method uses the singular value decomposition as the algebraic technique for matrix factorization that allows sorting the components by their level of relevance in the image composition. The process also presents a statistical analysis of colors, obtained from typical image samples, to form a probability map based on the color frequency, that further leads to a compact representation. Through the experiments executed on 2,700 frames of upper GI endoscopy, the coding method achieved an average compression ratio of 82.07% with a standard deviation of 6.37%, and objective quality of 38.85 dB with a standard deviation of 1.06 dB. In comparison with the H.263 coding standard, it was observed a statistically significant difference in terms of compression ratio and objective quality (p ? 0,05). As for the retrieval, 7212 frames were indexed and 97 sample images were used as search queries. As a result, an average precision of 94,85% was achieved for the configuration with best overall performance, outperforming other methods reported in the literature. Based on the results of the experiments it is possible to conclude that the proposed original method was effectively able to encode and automatically index medical examination videos for telemedicine applicationsDoutoradoFisiopatologia CirúrgicaDoutor em Ciência