Method for automated processing of digital image data

• US 20040057623A1
• Filed: 08/08/2003
• Published: 03/25/2004
• Est. Priority Date: 09/24/2002
• Status: Abandoned Application

• Current Assignee: Agfa-Gevaert AG (Agfa-Gevaert N.V.)
• Original Assignee: Agfa-Gevaert AG (Agfa-Gevaert N.V.)
• Inventors: Schuhrke, Thomas, Rother, Martin
• Application Number:
  US10/638,209
• Publication Number:
  US 20040057623A1
• Time in Patent Office: Days
• US Class Current: 382/232
• CPC Class Codes: H04N 1/40 Picture signal circuits H04N1/387 takes precedence
• CPC Class Codes: H04N 1/41 Bandwidth or redundancy reduction by scanning H04N1/17 ; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00
• CPC Class Codes: H04N 19/117 Filters, e.g. for pre-processing or post-processing sub-band filter banks H04N19/635
• CPC Class Codes: H04N 19/136 Incoming video signal characteristics or properties
• CPC Class Codes: H04N 19/14 Coding unit complexity, e.g. amount of activity or edge presence estimation H04N19/146 takes precedence
• CPC Class Codes: H04N 19/17 the unit being an image region, e.g. an object
• CPC Class Codes: H04N 19/172 the region being a picture, frame or field
• CPC Class Codes: H04N 19/176 the region being a block, e.g. a macroblock
• CPC Class Codes: H04N 19/18 the unit being a set of transform coefficients
• CPC Class Codes: H04N 19/186 the unit being a colour or a chrominance component
• CPC Class Codes: H04N 19/1883 the unit relating to sub-band structure, e.g. hierarchical level, directional tree, e.g. low-high [LH], high-low [HL], high-high [HH]
• CPC Class Codes: H04N 19/60 using transform coding
• CPC Class Codes: H04N 19/63 using sub-band based transform, e.g. wavelets
• CPC Class Codes: H04N 19/90 using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals

Abstract

The invention concerns a method for the automated processing of digital image data in which the input image data are in compressed form, and in which several method steps must be applied to the compressed image data, or to the image data decompressed before processing, within the scope of image processing. Based on the invention, control data from the still-compressed image data are collected that automatically control the image processing steps.

Claims

1. Method for automated processing of digital image data, wherein the input image data are in compressed form, and wherein several image-processing steps are applied to at least one of said compressed image data and, if the image data has been decompressed, said decompressed image data, the improvement wherein the image-processing steps are automatically controlled based upon control data extracted from the compressed image data.

2. Method as recited in claim 1, wherein the image-processing steps to be applied are selected based on the extracted control data.

3. Method as recited in claim 1, wherein image-processing parameters for the image-processing steps are selected based on the extracted control data.

4. Method as recited in claim 1, wherein control data are obtained from one of the color channels.

5. Method as recited in claim 1, wherein control data are obtained from the transformed, quantified, or de-quantified coefficients of the image data.

6. Method as recited in claim 5, wherein the control data are obtained from the transformed, quantified, or de-quantified coefficients of the overall image data.

7. Method as recited in claim 1, wherein the control data are obtained from the transformed, quantified, or de-quantified coefficients that correspond to the lowest frequencies (DC components for JPEG).

8. Method as recited in claim 7, wherein the control data are formed to provide a selection of the gradation characteristic curve to be used for the densities during reproduction of the digital image data.

9. Method as recited in claim 1, wherein the control data are obtained from the higher-frequency components of the transformed, quantified, or de-quantified coefficients (AC components for JPEG).

10. Method as recited in claim 9, wherein image data that include extremely high frequencies are identified as computer-generated graphics, and which are not corrected for film characteristics.

11. Method as recited in claim 9, wherein parameters for focus sharpening of image data are selected to be very small if the values of the high-frequency components are constantly very high.

12. Method as recited in claim 9, wherein parameters for focus sharpening are selected to be very small if the high frequency of the components of the compressed data are under-occupied.

13. Method as recited in claim 9, wherein parameters for focus sharpening of image data are selected based on the frequency progression of the components of the compressed data.

14. Method as recited in claim 9, wherein a fuzzy photograph is identified upon recognition of a direction tendency based on all components of the compressed data, and high frequencies are increased along the direction of camera motion.

15. Method as recited in claim 9, wherein larger fuzziness or graininess suppression parameters are selected when the components are dominated by lower frequencies.

16. Method as recited in claim 9, wherein the enlargement factor for the image reproduction is controlled based on the frequency spectrum.

17. Method as recited in claim 7, wherein the gradation characteristic curve or the Gamma value used for the processing is selected in dependence upon the ratio of high to low frequency components.

18. Method as recited in claim 5, wherein local control data are extracted from the transformed, quantified, or de-quantified coefficients of individual blocks of the compressed data.

19. Method as recited in claim 18, wherein the selection of filters and characteristic curves applied to the image data assigned to the block is performed in dependence upon the value of selected frequency ranges of the compressed image data of these blocks.

20. Method as recited in claim 18, wherein the selection of filters and characteristic curves applied to the image data assigned to the block is performed in dependence upon the ratio of high to low-frequency components of the compressed image data of these blocks.

21. Method as recited in claim 18, wherein the selection of filters and characteristic curves applied to the image data assigned to the block is performed in dependence upon the amassments of components of the compressed image data within the blocks.

22. Method as recited in claim 18, wherein the components of the compressed image data of chrominance images are evaluated by block in order to identify the motif.