Compression system for integrated sensor devices

• US 20070127831A1
• Filed: 01/31/2007
• Published: 06/07/2007
• Est. Priority Date: 04/10/2003
• Status: Active Grant

• Current Assignee: Karthik Venkataraman
• Original Assignee: Karthik Venkataraman
• Inventors: Venkataraman, Kartik
• Granted Patent: US 7,751,634 B2
• Time in Patent Office: Days
• US Class Current: 382/239
• CPC Class Codes: H04N 19/13 Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
• CPC Class Codes: H04N 19/593 involving spatial prediction techniques
• CPC Class Codes: H04N 19/60 using transform coding
• CPC Class Codes: H04N 19/90 using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals

Abstract

An imaging system incorporating adaptive compression which includes determining linear predictive differential residuals from an imager array pixel row. The differential residuals are classified into categories, each category having a range of differential residuals associated with it. The categories are analyzed to produce an ordered list having categories with most to least frequent residuals falling within a respective residual range associated with a respective category. The ordered list is then used to select a variable length encoding table with a matching ordered list. Variable length encoded category and range position offset data is output to a serializer unit, where the range position offset refers to a position in a range associated with a particular category.

Claims

1-20. -20. (canceled)

21. An imager system comprising:

an imaging array comprising a plurality of pixels;

a processor circuit for outputting digital pixel signals representing incident light on the pixels of said array; and

a compression circuit for compressing said digital pixel signals by encoding said pixel signals such that successive pixel signals in each row are encoded as first and second transmission data determined based upon a plurality of first and second data, wherein;

said first data is based on differences between a plurality of groups of at least two pixel signals;

said second data is determined based upon at least one categorization of said first data values by predetermined ordered ranges, said second data comprising an ordered sequence of category identifiers by frequency of occurrence within said ranges;

said first transmission data being assigned to said pixel signals from a selected codeword data set of a plurality of codeword data sets, said selected codeword data set being selected by matching said second data with one of said plurality of codeword data sets; and

said second transmission data is determined for each pixel signal based upon a category identifier assigned to said pixel and position within a said range associated with said category identifier.

22. An imager system of claim 21 wherein said plurality of first data comprises linear prediction differential residual data values for each channel of said plurality of pixels in a row.

23. An imager system of claim 21, wherein said plurality of second data comprises an ordered list of category identifiers for linear prediction differential residual data value ranges corresponding to said first data values.

24. An imager system of claim 21 wherein said second transmission data is a range position offset based upon ordered ranges associated with a category identifier, said offset relates to a position from a beginning of the ordered range associated with an associated category identifier.

25. An imager system of claim 21, wherein said codeword data sets comprise more than one variable length codeword.

26. An imager system of claim 21, wherein said codeword data sets comprise Huffman codes.

27. An imager system of claim 21 wherein said codeword data sets comprise Huffman coding tables, wherein each coding table stores codewords associated with a category identifier, said codewords are ordered from a most frequently occurring category identifier having a smallest codeword bit-size value to a least frequently occurring category identifier having a largest bit-size codeword value.

28. An imager system of claim 21 wherein said plurality of second data is based upon probability density function data for said plurality of first data.

29. An imager system comprising:

an imaging array comprising a plurality of pixels;

a pixel signal processor circuit for outputting digital pixel signals representing incident light on the pixels of said array; and

a compression circuit for compressing said digital pixel signals by encoding said pixel signals such that successive pixel signals in each row are encoded with a codeword and a range position offset, said codewords determined based upon a plurality of first and second data, wherein;

said first data is based on differences between a plurality of groups of at least two pixel signals;

said second data being determined based upon at least one categorization of said first data values by predetermined ordered ranges, said second data comprising an ordered sequence of category identifiers by frequency of occurrence associated within said ranges;

said codewords being assigned to said pixel signals from a selected codeword data set of a plurality of codeword data sets, said selected codeword data set being selected by matching said second data with one of said plurality of codeword data sets, each set comprises one or more category identifiers ordered with a different sequence than identifiers in other of said sets and an associated codeword; and

said range position offset for each pixel signal being determined based on a category identifier assigned to said pixel and the position within said range associated with said assigned category identifier.

30. An imager system of claim 29 wherein said plurality of first data comprises linear prediction differential residual data values for each channel of said plurality of pixels in a row.

31. An imager system of claim 29, wherein said plurality of second data comprises an ordered list of category identifiers for linear prediction differential residual data value ranges corresponding to said first data values.

32. An imager system of claim 29, wherein said range position offset is based upon ordered ranges associated with a category identifier, said offset being related to position from the beginning of the ordered range associated with an associated category identifier.

33. An imager system of claim 29, wherein said coding tables comprise more than one variable length codeword.

34. An imager system of claim 29, wherein said codeword data sets comprise Huffman code tables.

35. An imager system of claim 29, further comprising a CMOS sensor.

36. An imager system of claim 29, further comprising a CCD sensor.

37. An imager system of claim 29, wherein said plurality of second data is based upon probability density function data for said plurality of first data.

38. An imager system comprising:

an imager array comprising rows of pixels;

a plurality of first data, a plurality of second data and a plurality of third data;

a memory for storing a plurality of coding tables, each coding table comprising codewords ordered based on a frequency of occurrence of a plurality of classification data identifiers; and

compression logic, said compression logic selecting one of a plurality of coding tables based on a match between said plurality of second data and said plurality of classification data identifiers,

wherein said compression logic encodes signals from said imaging pixels based upon said selected coding table and said plurality of first, second and third data.

39. An imager system of claim 38, wherein said first data comprises a plurality of differences between a plurality of sequential pixel sets within a pixel row, said second data is based on a plurality of pixel signal classification data, said classification data are associated with ranges of first data values, said second data comprising an ordered list of one or more said classification data identifiers, a classification identifier is being added to said ordered list when a said first data values falls within an associated range;

said third data is determined in relation to a position that a said first data occupies within a said range.

40. An imager system of claim 38, wherein said plurality of first data comprises linear prediction differential residual data values for each channel of said imaging structures in a row.

41. An imager system of claim 38, wherein said plurality of second data comprises an ordered list of classification identifiers for linear prediction differential residual data value ranges.

42. An imager system of claim 38, wherein said third data value comprises an offset that is based upon ordered ranges associated with a classification identifier, said offset relates to a position from a beginning of the ordered range associated with an associated classification identifier.

43. An imager system of claim 38, wherein said coding tables comprise more than one variable length codeword.

44. An imager system of claim 38 wherein said coding tables comprise Huffman coding tables, wherein each coding table stores codewords, said codewords being associated with a classification identifier, said codewords are ordered with a most frequently occurring classification identifier having the shortest codeword value and the least frequently occurring classification identifier having the longest codeword value.

45. An imager system of claim 38, wherein said plurality of second data is based upon probability density function data for said plurality of first data.

46. An image processing system comprising an image processing unit comprising:

compression logic; and

a memory for storing of a plurality of coding tables, each table comprising codewords in a sequence based on frequency of occurrence of symbol values;

wherein said compression logic encodes imager signal data from a plurality of imaging structures based upon a said coding table and a plurality of first, second and third data.

47. An image processing system of claim 46, wherein said first data values comprise a plurality of difference values determined based upon a plurality of sets of signals, each signal being derived from a different set of two said imaging structures, said second data values are determined based upon ordered ranges of first data values, said second data values ordered by frequency that a first data value falls within one of said ranges, each third data value is determined based upon a said first data value and a position within one of said ranges.

48. An imager comprising:

an imaging pixel array;

a processing unit for processing pixel signals from said pixel array, said unit comprising;

a first memory for storing a plurality of Huffman coding tables, contents of said tables being determined based on a plurality of different variation patterns between predetermined pixel row values, each of said coding tables comprising a plurality of category identifiers and associated Huffman codewords;

an image storage memory for storing pixel data from a selected row of pixels in said pixel array; and

a second memory for storing encoded row data for output,

wherein said unit calculates pixel differentials for pixels in a selected row and probability density function data associated with categories of difference values, each category defining a range of said differentials, said unit encodes as an output said pixel data from a selected row based on a selected one of said Huffman coding tables, said Huffman coding table being selected based upon probability density function data.

49. A method of performing image data compression comprising:

sampling image data from a plurality of pixels;

determining difference data for a plurality of first and second image data sets obtained from said image data;

determining distribution data for said difference data, said distribution data being based upon frequency of occurrence of difference data within ranges of difference data, said distribution data being organized into categories, each category having a category identifier;

selecting a variable length coding table with a matching distribution data from a plurality of variable length coding tables, each having different distribution data;

determining a category identifier and a range position identifier associated with each of said difference data; and

outputting a codeword and range position identifier for each of said difference data.

50. A method of claim 49, wherein said range position identifier is determined based upon where difference data falls within a range associated with a related said category identifier which said difference data falls within.

51. A method of claim 49, wherein said codeword is determined by matching a category identifier with said selected table.

52. A method as in claim 49, wherein said codewords are variable length codewords based on a frequency of occurrence of said difference data by category identifier.

53. A method as in claim 49, wherein said codewords are Huffman coded.

54. A method of performing image data compression comprising

reading a plurality of signals from a selected pixel row in an image sensor;

calculating a plurality of linear predictive differential residuals based on differences between at least two or more of said plurality of signals from the selected row;

calculating a plurality of probability density data based on said residuals, each of said categories being associated with an ordered range of residual data values;

selecting a variable length code table with a matching probability density data from a plurality of variable length code tables with different sequences of probability density data;

determining a category and a range position offset value for each residual data, said range position offset value relates to a position that an associated residual is located at in an associated range, said position being determined from the beginning of said range; and

outputting each category as a codeword and an associated range position offset for each said residual, said codeword determined by matching a category identifier to a codeword in a selected table.

55. A method of performing image data compression comprising:

reading a plurality of pixel signals from a selected pixel row in an image sensor;

determining a plurality of first data based on differences between a plurality of groups of at least two pixel signals;

determining a plurality of second data based upon at least one categorization of said first data, said second data comprising an ordered sequence of category identifiers by frequency of occurrence within an ordered range;

determining first transmission data for each pixel signal from a selected codeword data set of a plurality of codeword data sets, said selected codeword data set being selected by matching said second data with one of said plurality of codeword data sets;

determining a second transmission data for each pixel signal based upon a said category identifier assigned to said pixel and position within a range associated with said category identifier; and

outputting encoded pixel signals such that successive said pixel signals in each row are encoded with said first and second transmission data.

56. A method as in claim 55, wherein said plurality of first data comprises linear prediction differential residual data values for each channel of said plurality of pixels in a row.

57. A method as in claim 55, wherein said plurality of second data comprises an ordered list of category identifiers for linear prediction differential residual data value ranges corresponding to said first data.

58. A method as in claim 55, wherein said second transmission data is a range position offset based upon ordered ranges associated with a category identifier.

59. A method as in claim 55, wherein said codeword data sets comprise more than one variable length codeword.

60. A method as in claim 55, wherein said codeword data sets comprise tables comprising Huffman codes.

61. A method as in claim 55, wherein said codeword data sets comprise Huffman coding tables, wherein each coding table stores codewords, said codewords being associated with a category identifier, said codewords are ordered from a most frequently occurring category identifier having a shortest codeword value to a least frequently occurring category identifier having a longest codeword value.

62. A method as in claim 55, wherein said plurality of second data is based upon probability density function data for said plurality of first data.

63-69. -69. (canceled)