Header-based processing of images compressed using multi-scale transforms

A method comprising:

• receiving header information corresponding to a bit stream of multi-scale transform-based compressed data representing image data;
• generating a feature vector corresponding to image description bits in the bit stream from the header information;
• and
• performing one or more operations on at least a portion of the bit stream based on the feature vector.

An apparatus comprising:

• means for receiving header information corresponding to a bit stream of multi-scale transform-based compressed data representing image data;
• means for generating a feature vector corresponding to image description bits in the bit stream from the header information;
• and
• means for performing one or more operations on at least a portion of the bit stream based on the feature vector.

An article of manufacture having one or more recordable medium with executable instructions stored thereon which, when executed by a system, cause the system to:

• receive header information corresponding to a bit stream of multi-scale transform-based compressed data representing image data;
• generate a feature vector corresponding to image description bits in the bit stream from the header information;
• and
• perform one or more operations on at least a portion of the bit stream based on the feature vector.

A method for segmenting an image comprising:

• receiving a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block, assigning to the block a scale from a set of scales that maximizes a cost function, wherein the cost function is a product of a total likelihood and a prior, wherein the total likelihood is a product of likelihoods calculated using the header of the block;
• and
• segmenting the image by grouping together blocks that have been assigned equivalent scales.

A method for adaptively scaling an image comprising:

• receiving a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block, determining that the block retains significance at a scale upon determining that an entropy of a multi-scale coefficient of a block at the scale is greater than a mean entropy of multi-scale coefficients of blocks in at least one coarser scale;
• and
• scaling the image to a coarsest scale at which a threshold percentage of the blocks retain significance at the scale.

A method for automatically scaling and cropping an image, comprising:

• receiving a file that contains a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block and for each scale of a set of scales:
• setting a cumulative entropy distribution for the block at a scale equal to a weighted summation of a number of bits spent to code the block for scales at and between a first scale and a maximum scale;
• and
• for each width and height offset within a given image width and height, setting an indicator function of the block at the chosen scale and chosen width and height offsets to one upon determining that a width location of the block is not greater than a first minimum value and a height location of the block is not greater than a second minimum value, wherein the first minimum value is a minimum value of a set consisting of a chosen width offset and a sum of the chosen width offset with the display width scaled by the first scale, and wherein the second minimum value is a minimum value of a set consisting of a chosen height offset and a sum of the chosen height offset with the display height scaled by the first scale;
• computing a location and scale that together maximize a summation consisting of the cumulative entropy distribution for the block at the optimal scale multiplied with an indicator function of the block and by a parameter;
• and
• cropping the image to the optimal location and down-sampling a resulting cropped image to the optimal scale.

An article of manufacture having one or more recordable media with executable instructions stored thereon which, when executed by a machine, cause the machine to:

• receive a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block, assign to the block a scale from a set of scales that maximizes a cost function, wherein the cost function is a product of a total likelihood and a prior, wherein the total likelihood is a product of likelihoods of the blocks;
• and
• segment the image by grouping together blocks that have been assigned equivalent scales.

An article of manufacture having one or more recordable media with executable instructions stored thereon which, when executed by a machine, cause the machine to:

• receive a file that contains a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block, determine that the block retains significance at a scale upon determining that an entropy of a multi-scale coefficient of a block at the scale is greater than a mean entropy of multi-scale coefficients of blocks in at least one coarser scale;
• and
• scale the image to a coarsest scale at which a threshold percentage of the blocks retain significance at the scale.

An article of manufacture having one or more machine-readable media storing executable instruction thereon which, when executed by a machine, cause the machine to:

• receive a header that contains multi-scale entropy distribution information on blocks of an image;
• for each block and for each first scale of a set of scales:
• set a cumulative entropy distribution for the block at the first scale equal to a summation of a number of bits spent to code the block for scales at and between the first scale and a maximum scale;
• and
• set an indicator function of the block and the first scale to one upon determining that a width of the block is not greater than a first minimum value and a height of the block is not greater than a second minimum value and to zero otherwise, wherein the first minimum value is a minimum value of a set consisting of a width of the image and a sum of the width of the block plus one plus a desired height scaled by the first scale, and wherein the second minimum value is a minimum value of a set consisting of a height of the image and a sum of the height of the block plus one plus a desired width scaled by the first scale;
• compute an optimal location and an optimal scale that together maximize a summation, for each block in the optimal location at the optimal scale, of the cumulative entropy distribution for the block at the optimal scale, multiplied by the indicator function of the block and the optimal scale, multiplied by a parameter;
• and
• crop the image to the optimal location and down-sampling a resulting cropped image to the optimal scale.

An apparatus comprising:

• a receiving unit to receive a header that contains multi-scale entropy distribution information on blocks of an image;
• and
• a processing unit coupled with the receiving unit, the processing unit to
• for each block, assign to the block a scale from a set of scales that maximizes a cost function, wherein the cost function is a product of a total likelihood and a prior, wherein the total likelihood is a product of likelihoods of the blocks;
• and
• group together blocks that have been assigned equivalent scales to segment the image.

An apparatus to adaptively scale an image, comprising:

• a receiving unit to receive a header that contains multi-scale entropy distribution information on blocks of an image;
• and
• a processing unit coupled with the receiving unit, the processing unit to
• for each block, determine that the block retains significance at a scale upon determining that an entropy of a multi-scale coefficient of a block at the scale is greater than a mean entropy of multi-scale coefficients of blocks in at least one coarser scale;
• and
• scale the image to a coarsest scale at which a threshold percentage of the blocks retain significance at the scale.

An apparatus to automatically scale and crop an image, comprising:

• a receiving unit to receive a header that contains multi-scale entropy distribution information on blocks of an image;
• and
• a processing unit coupled with the receiving unit, the processing unit to
• for each block and for each first scale of a set of scales;
• set a cumulative entropy distribution for the block at the first scale equal to a summation of a number of bits spent to code the block for scales at and between the first scale and a maximum scale;
• and
• set an indicator function of the block and the first scale to one upon determining that a width of the block is not greater than a first minimum value and a height of the block is not greater than a second minimum value and to zero otherwise, wherein the first minimum value is a minimum value of a set consisting of a width of the image and a sum of the width of the block plus one plus a desired height scaled by the first scale, and wherein the second minimum value is a minimum value of a set consisting of a height of the image and a sum of the height of the block plus one plus a desired width scaled by the first scale;
• compute an optimal location and an optimal scale that together maximize a summation, for each block in the optimal location at the optimal scale, of the cumulative entropy distribution for the block at the optimal scale, multiplied by the indicator function of the block and the optimal scale, multiplied by a parameter;
• and
• crop the image to the optimal location and down-sample a resulting cropped image to the optimal scale.