RESUMO
Graphics are powerful tools to communicate research results and to gain information from data. However, researchers should be careful when deciding which data to plot and the type of graphic to use, as well as other details. The consequence of bad decisions in these features varies from making research results unclear to distortions of these results, through the creation of "chartjunk" with useless information. This paper is not another tutorial about "good graphics" and "bad graphics". Instead, it presents guidelines for graphic presentation of research results and some uncommon, but useful examples to communicate basic and complex data types, especially multivariate model results, which are commonly presented only by tables. By the end, there are no answers here, just ideas meant to inspire others on how to create their own graphics.
Assuntos
Gráficos por Computador/normas , Interpretação Estatística de Dados , Disseminação de Informação/métodos , Guias de Prática Clínica como Assunto , Projetos de Pesquisa/normas , Pesquisa Biomédica/métodos , Pesquisa Biomédica/normas , Pesquisa Biomédica/estatística & dados numéricos , Brasil/epidemiologia , Humanos , Hanseníase/epidemiologia , Neoplasias/epidemiologia , Editoração/normas , Projetos de Pesquisa/estatística & dados numéricos , Inquéritos e Questionários/normasRESUMO
In this paper, we explore H.264/AVC operating in intraframe mode to compress a mixed image, i.e., composed of text, graphics, and pictures. Even though mixed contents (compound) documents usually require the use of multiple compressors, we apply a single compressor for both text and pictures. For that, distortion is taken into account differently between text and picture regions. Our approach is to use a segmentation-driven adaptation strategy to change the H.264/AVC quantization parameter on a macroblock by macroblock basis, i.e., we deviate bits from pictorial regions to text in order to keep text edges sharp. We show results of a segmentation driven quantizer adaptation method applied to compress documents. Our reconstructed images have better text sharpness compared to straight unadapted coding, at negligible visual losses on pictorial regions. Our results also highlight the fact that H.264/AVC-INTRA outperforms coders such as JPEG-2000 as a single coder for compound images.