{"title":"Titelei/Inhaltsverzeichnis","authors":"Sophie Eisentraut","doi":"10.5771/9783748909347-1","DOIUrl":"https://doi.org/10.5771/9783748909347-1","url":null,"abstract":"","PeriodicalId":117118,"journal":{"name":"Talking Democracy at the United Nations","volume":"s3-46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130213978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.5771/9783748909347-254
Sophie Eisentraut
In order to give the reader a avor of how the JPEG algorithms aaect the reconstructed images, we attached three pairs, an original image and its diierence image generated from our simulation. In the beginning, we tried to take photographs of the original images and the decoded images. But it turns out that it is diicult to see on the photographs the diierences between the original image and the decoded image, although they are visible on the screen. Therefore, instead, we have attached pictures of the diierence between the original image and the decoded image. The original images are visually better on the screen than on the photograph due to limitations of the photographic techniques. Most of the reconstructed images are blurry compared to the original ones. The example luminance quantization table (see Table 3.2) has a better impact on the images Tree, Sailboat, Airplane and Aerial than the images House, Splash, Girl, Lenna and Tiiany. There are visible block edge eeects and obvious blurring in the latter decoded images. Since the absolute diierence images are too obscure to show on the photographs, we exaggerated these diierences in the following way: For each pixel compute a dii, the absolute value of the diierence between the original image and the decoded image. In a given image, there are very few diis in the range 128255. Map those values to pixel value 255. On the screen those will show up as very bright spots, but on the photographs they are not so obvious. Find the minimum and maximum of all the diis in a given image. Exaggerate the diierences. If a dii is in the range 0127, then map it to 0255 as follows: 255 * (dii ? min)/(max ? min) In the exaggerated diierence images it is easy to see the eeects of the JPEG algorithms.
{"title":"7. APPENDIX","authors":"Sophie Eisentraut","doi":"10.5771/9783748909347-254","DOIUrl":"https://doi.org/10.5771/9783748909347-254","url":null,"abstract":"In order to give the reader a avor of how the JPEG algorithms aaect the reconstructed images, we attached three pairs, an original image and its diierence image generated from our simulation. In the beginning, we tried to take photographs of the original images and the decoded images. But it turns out that it is diicult to see on the photographs the diierences between the original image and the decoded image, although they are visible on the screen. Therefore, instead, we have attached pictures of the diierence between the original image and the decoded image. The original images are visually better on the screen than on the photograph due to limitations of the photographic techniques. Most of the reconstructed images are blurry compared to the original ones. The example luminance quantization table (see Table 3.2) has a better impact on the images Tree, Sailboat, Airplane and Aerial than the images House, Splash, Girl, Lenna and Tiiany. There are visible block edge eeects and obvious blurring in the latter decoded images. Since the absolute diierence images are too obscure to show on the photographs, we exaggerated these diierences in the following way: For each pixel compute a dii, the absolute value of the diierence between the original image and the decoded image. In a given image, there are very few diis in the range 128255. Map those values to pixel value 255. On the screen those will show up as very bright spots, but on the photographs they are not so obvious. Find the minimum and maximum of all the diis in a given image. Exaggerate the diierences. If a dii is in the range 0127, then map it to 0255 as follows: 255 * (dii ? min)/(max ? min) In the exaggerated diierence images it is easy to see the eeects of the JPEG algorithms.","PeriodicalId":117118,"journal":{"name":"Talking Democracy at the United Nations","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130887360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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