Abstract: A method is for compressing a digital image including a matrix of elements, with each element including at least one component of a different type for representing a pixel. The method includes splitting the digital image into a plurality of blocks, and calculating for each block a group of discrete cosine transform (DCT) coefficients for the components of each type, and quantizing the DCT coefficients of each group using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method also includes further quantizing the DCT coefficients of each group using the corresponding quantization table scaled by a pre-set factor, and arranging the further quantized DCT coefficients in a zig-zig vector.

Abstract: A method for compression includes applying a discrete cosine transform to said image data to obtain transform coefficients, quantizing the transform coefficients by applying a quantization level scaled through a gain value, adjusting the gain value as a function of desired image parameters by executing a first time said quantization operation applying a first gain value and obtaining first quantized data, estimating statistically a second gain value suitable to obtain the desired image parameters, and executing a second time said quantization operation applying said second gain value. The operation of statistical estimation of the second gain value includes evaluating a threshold value as a function of the desired image parameters and setting to zero a percentage of coefficients of the first quantized data as a function of the threshold value.

Abstract: A method for image compression of a set of image data includes performing a quantization operation on the image data. The quantization operation may include controlling a compression factor by applying a scaled quantization level obtained by multiplying a first quantization level by a gain factor. The gain factor may be updated as a function of a bit per pixel value of a compressed image. The update operation may include an iterative procedure including at least one iteration step that provides for updating a current gain factor as a function of a previous gain used for performing a previous compression step and as a function of a ratio of the bit per pixel value of the compressed image at the previous compression step to a target bit per pixel value. The method may be used in Joint Photographic Experts Group (JPEG) image processing and digital still cameras.

Abstract: The method for block coding data, such as video data, via a compression operation includes applying to input-data blocks a discrete-cosine-transform (DCT) operation and a quantization operation to produce compressed-data blocks. The compressed-data blocks are subjected to a coding operation to obtain compressed output flows; and an inverse-quantization operation and an inverse-discrete-cosine-transform (IDCT) operation are applied on the compressed-data blocks to obtain reconstructed blocks. The method includes controlling generation of mismatch errors from the input-data blocks by detecting data blocks from the input-data blocks and compressed-data blocks that are liable to cause mismatch errors, and modifying the blocks that are liable to cause mismatch errors prior to the coding operation.

Abstract: Subdivision per basic color channels of grey level data generated by a color sensor is no longer required according to a novel color interpolation method of an image acquired by a digital color sensor generating grey levels for each image pixel as a function of the filter applied to the sensor by interpolating the values of missing colors of each image pixel for generating triplets or pairs of values of primary colors or complementary base hues for each image pixel. The method may include calculating spatial variation gradients of primary colors or complementary base hues for each image pixel and storing the information of directional variation of primary color or complementary base hue in look-up tables pertaining to each pixel.

Abstract: A method is for compressing a digital image that is made up of a matrix of elements, with each element including a plurality of digital components of different types for representing a pixel. The method includes splitting the digital image into a plurality of blocks, and calculating for each block a group of DCT coefficients for the components of each type, and quantizing the DCT coefficients of each block using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method further includes determining at least one energy measure of the digital image, and estimating the gain factor as a function of the at least one energy measure. The function is determined experimentally according to the target compression factor.

Abstract: In the method, the membership image is produced through a multi-stage process that contemplates a first punctual classification stage generating a rough membership image, followed by a low-pass filtering of regionalization of the image through a down-sampling of the image followed by an up-sampling of the image, and in a further step of merging the regions that overcomes the superposition effects induced by the low-pass filtering operation. The so generated membership image contains semantic information of the input image that may be used for effectively implementing an adaptive correction/improvement of the color of the input image. For an adaptive correction/improvement of the color, the encoded data of the generated membership image is fed in parallel to the data of the real image to an adaptive modification block of the color of the image pixels.

Abstract: A fast method of color interpolation of pixels of an image acquired by a color filtered digital sensor uses a very simple cost function that nevertheless produce interpolated images of good quality. The cost function is computationally simpler because it does not require the calculation of powers and square roots. The triangulation algorithm may be executed in far less time, while practically ensuring the same performance. The triangulation algorithm on average may use only two iteration steps. The interpolation process may be followed by an anti-aliasing processing that effectively removes color artifacts.

Abstract: For each color channel, the process includes gathering Bayer pattern pixel values by pairs, each pair being composed by two successive pixels belonging to the channel along the scanning direction of the pixels of the image, thus each pair of values representing a current input vector, and calculating a predictor vector of the input vector in terms of the differences between the values defining the input vector and a pair of prediction values generated according to a certain criterion, for representing a prediction error. The process further includes quantizing each so calculated predictor vector according to a heavier or lighter degree of quantization depending on whether the predictor vector is representative of an area of relatively uniform color of the image or of an area of relatively abrupt changes of colors of the image, and generating a multibit code representative of the quantized predictor vector of the input vector according to a certain compression ratio.

Abstract: A method for correcting an image from defects and filtering from Gaussian noise corrects each pixel of the image when it is considered defective and filters it from Gaussian noise in one-pass. The one-pass improves the speed for performing the correcting and filtering. The drawbacks associated with choosing incompatible defect correction and filtering operations are overcome.

Abstract: A method compresses a digital image including a matrix of elements each one including a plurality of digital components of different type representing a pixel. The method includes the steps of providing an incomplete digital image wherein at least one component is missing in each element, obtaining the digital image from the incomplete digital image, splitting the digital image into a plurality of blocks and calculating, for each block, a group of DCT coefficients for the components of each type, and quantizing the DCT coefficients of each group using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method further comprises the steps of determining an energy measure of the incomplete digital image and estimating the gain factor as a function of the energy measure, the function being determined experimentally according to the target compression factor.

Abstract: A method compresses a digital image including a matrix of elements each one including a plurality of digital components of different type representing a pixel. The method includes the steps of providing an incomplete digital image wherein at least one component is missing in each element, obtaining the digital image from the incomplete digital image, splitting the digital image into a plurality of blocks and calculating, for each block, a group of DCT coefficients for the components of each type, and quantizing the DCT coefficients of each group using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method further comprises the steps of determining an energy measure of the incomplete digital image and estimating the gain factor as a function of the energy measure, the function being determined experimentally according to the target compression factor.

Abstract: A method is for compressing a digital image that is made up of a matrix of elements, with each element including a plurality of digital components of different types for representing a pixel. The method includes splitting the digital image into a plurality of blocks, and calculating for each block a group of DCT coefficients for the components of each type, and quantizing the DCT coefficients of each block using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method further includes determining at least one energy measure of the digital image, and estimating the gain factor as a function of the at least one energy measure. The function is determined experimentally according to the target compression factor.

Abstract: A method is for compressing a digital image including a matrix of elements, with each element including at least one component of a different type for representing a pixel. The method includes splitting the digital image into a plurality of blocks, and calculating for each block a group of discrete cosine transform (DCT) coefficients for the components of each type, and quantizing the DCT coefficients of each group using a corresponding quantization table scaled by a gain factor for achieving a target compression factor. The method also includes further quantizing the DCT coefficients of each group using the corresponding quantization table scaled by a pre-set factor, and arranging the further quantized DCT coefficients in a zig-zig vector.