Abstract: A method and a system for mental index prediction are provided. The method includes the following steps. A plurality of images of a subject person are obtained. A plurality of emotion tags of the subject person in the images are analyzed. A plurality of integrated emotion tags in a plurality of predetermined time periods are calculated according to the emotion tags respectively corresponding to the images. A plurality of preferred features are determined according to the integrated emotion tags. A mental index prediction model is established according to the preferred features to predict a mental index according to the emotional index prediction model.

Abstract: A method for image compression and a circuit system thereof are provided. In the method, pixel values of an image are obtained. A compression scenario is decided, for example, a uniform-quantization manner or a non-uniform-quantization manner is used for an M-bit image being compressed to an N-bit image so as to decide codeword sections for the image. Every codeword section has a codeword distance. The codeword sections have a fixed codeword distance in the uniform-quantization manner. Alternatively, in the non-uniform-quantization manner, the image can be divided into multiple codeword sections having different codeword distances according to a brightness distribution. Afterwards, a random number is generated for deciding codeword and index for original value of each of the pixels. An index table is accordingly formed. The index table is provided for obtaining the codeword in a decoding process by querying a codebook with the index.

Abstract: The present invention provides a compression method, wherein the compression method includes the steps of: setting a quantization function; setting a plurality of scaling ratios; receiving image data; for a block of a frame of the image data, using a conversion matrix to convert data of the block to generate a plurality of converted data; determining a specific scaling ratio from the plurality of scaling ratios according to the plurality of converted data; and using the quantization function to perform a quantization operation on a plurality of adjusted data to generate compressed data, wherein the plurality of adjusted data are generated according to the plurality of converted data and the specific scaling ratio.

Abstract: An image recognition system includes a color conversion module and a target recognition module. The color conversion module is configured to convert a gray-level image into a preset color image according to a conversion function. The target recognition module includes a machine learning algorithm, and the machine learning algorithm includes a plurality of functions and a plurality of parameters. The machine learning algorithm receives the preset color image, and outputs a recognition result according to the functions and the parameters, the recognition result including an existent target or a null target.

Abstract: A method and a system for image correction are provided. The system includes an image processor that performs the method for image correction when receiving an input image. In the method, an overexposed area within the image is determined. The channel values of pixels in the overexposed area are applied with corresponding weights. An automatic white balance process is performed on the channel values of pixels in the overexposed area according to an expectation of low saturation in the overexposed area. Besides a first channel value of each of the pixels, a second channel value and a third channel value of the pixel can be estimated according to the channel values of periphery pixels. A correction value overexposed of the first channel value of the current pixel can be calculated according to the corresponding weights of the pixels in the overexposed area.

Abstract: A circuitry for image demosaicing and contrast enhancement and an image-processing method thereof are provided. The circuitry includes a storage device that is used to temporarily store an image and is jointly used by circuits that perform color restoration and brightness reconstruction. The circuitry includes a color restoration circuit for performing image interpolation and a global mapping circuit that performs mapping to obtain brightness of an image according to restored red, green and blue information of every pixel. Further, an edge texture feature decision circuit is provided to obtain each pixel's directionality for color restoration. A brightness estimation circuit utilizes green information of the pixels as the brightness for an area. After that, a color image with the color restoration and brightness reconstruction is outputted.

Abstract: The present invention discloses an image data augmentation method that includes the steps outlined below. At least one distortion operation function is retrieved. A plurality of pixels included in the image are twisted according to the distortion operation function to generate at least one augmented image. Object information of each of at least one object included in the image is converted according to the distortion operation function to generate object information conversion result. The augmented image, a class tag of each of the at least one object and the object information conversion result are fed to a machine learning module to generate a machine learning result.

Abstract: A method for determining deblur filter coefficients includes the following steps: generating an edge profile according to the data of a blurred image; estimating a blur kernel according to the edge profile, wherein the blur kernel indicates how an imaging process blurs original image data and thereby generates blurred image data; and determining coefficients of a deblur filter to make a process result of the deblur filter processing the blurred image data approach the original image data.

Abstract: A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

Abstract: A method for image compression and a circuit system thereof are provided. In the method, pixel values of an image are obtained. A compression scenario is decided, for example, a uniform-quantization manner or a non-uniform-quantization manner is used for an M-bit image being compressed to an N-bit image so as to decide codeword sections for the image. Every codeword section has a codeword distance. The codeword sections have a fixed codeword distance in the uniform-quantization manner. Alternatively, in the non-uniform-quantization manner, the image can be divided into multiple codeword sections having different codeword distances according to a brightness distribution. Afterwards, a random number is generated for deciding codeword and index for original value of each of the pixels. An index table is accordingly formed. The index table is provided for obtaining the codeword in a decoding process by querying a codebook with the index.

Abstract: Disclosed is an image processing method based on sensor characteristics. The method includes the following steps: obtaining a constant of a relation “c??(x)/?{square root over (x)}” between output signals “x” and the noise standard deviation “?(x)” of the output signals, in which the output signals are outputted by a sensor based on a sensor gain such as an ISO value of a camera; and calculating an output value of a target pixel according to pixel values within a sampling window, the constant, and a front-end gain such as an auto exposure gain, in which the pixel values include an input value of the target pixel.

Abstract: A method and a system for pixel channel imbalance compensation for an image sensing circuit are provided. The system includes an image acquisition circuit having a lens, a color filter and an image sensor and a processing circuit. In the method performed by the processing circuit, a second frame image is retrieved from a motion image, and a first frame image that has undergone noise reduction can be retrieved from a memory. Motion detection is performed between the frames by comparing the first frame image and the second frame image. The motion detection is referred to as a reference for determining how to perform 3D noise reduction. A compensation value for channel imbalance between the adjacent channels can be estimated based on the image under noise reduction in a same buffer. While the pixel channel imbalance is compensated, the image is then restored by an interpolation method.

Abstract: Disclosed are a color reconstruction device and method capable of accurately recovering the missing color of a target pixel. The device includes: a direction-characteristic estimation circuit calculating a horizontal-variation characteristic and a vertical-variation characteristic according to a first color of a target pixel and the values of pixels within a reference range, in which the target pixel is in the reference range and a current value of the target pixel is a first color value; an edge-texture decision circuit determining which of N predetermined relations matches the relation between the horizontal-variation characteristic and the vertical-variation characteristic and thereby determining the directional characteristic of the target pixel, in which the N is not less than four; and a color recovery circuit calculating a second and a third color values of the target pixel according to the directional characteristic and the values of the pixels within the reference range.

Abstract: A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

Abstract: A denoising method based on a signal-to-noise ratio (SNR), which includes: obtaining a current input coefficient; obtaining a current noise standard deviation by querying a first relationship table; querying a second relationship table according to the current noise standard deviation and the current input coefficient to obtain a current slope corresponding to the current input coefficient; generating a current output coefficient by multiplying the current input coefficient and a compression magnification function; and calculating the current output coefficient by substituting the current noise standard deviation, the current input coefficient, and the current slope.

Abstract: A method and a system for image correction are provided. The system includes an image processor that performs the method for image correction when receiving an input image. In the method, an overexposed area within the image is determined. The channel values of pixels in the overexposed area are applied with corresponding weights. An automatic white balance process is performed on the channel values of pixels in the overexposed area according to an expectation of low saturation in the overexposed area. Besides a first channel value of each of the pixels, a second channel value and a third channel value of the pixel can be estimated according to the channel values of periphery pixels. A correction value overexposed of the first channel value of the current pixel can be calculated according to the corresponding weights of the pixels in the overexposed area.

Abstract: Disclosed is an image processing method based on sensor characteristics. The method includes the following steps: obtaining a constant of a relation “c??(x)/?{square root over (x)}” between output signals “x” and the noise standard deviation “?(x)” of the output signals, in which the output signals are outputted by a sensor based on a sensor gain such as an ISO value of a camera; and calculating an output value of a target pixel according to pixel values within a sampling window, the constant, and a front-end gain such as an auto exposure gain, in which the pixel values include an input value of the target pixel.

Abstract: A method of updating background model for an image processing device is disclosed. The method includes receiving at least a background image, counting a number of occurrences of a color of each pixel of the background image according to a color information of each pixel of the at least a background image, to establish a background model including a statistical color information about the color and the number of occurrences of the color, receiving a current image, and determining whether to update the number of occurrences of the color in the background model corresponding to a color of each pixel of the current image according to a color information of each pixel of the current image and a result of comparing a random number with a threshold.

Abstract: A method and a system for pixel channel imbalance compensation for an image sensing circuit are provided. The system includes an image acquisition circuit having a lens, a color filter and an image sensor and a processing circuit. In the method performed by the processing circuit, a second frame image is retrieved from a motion image, and a first frame image that has undergone noise reduction can be retrieved from a memory. Motion detection is performed between the frames by comparing the first frame image and the second frame image. The motion detection is referred to as a reference for determining how to perform 3D noise reduction. A compensation value for channel imbalance between the adjacent channels can be estimated based on the image under noise reduction in a same buffer. While the pixel channel imbalance is compensated, the image is then restored by an interpolation method.

Abstract: A denoising method based on a signal-to-noise ratio (SNR), which includes: obtaining a current input coefficient; obtaining a current noise standard deviation by querying a first relationship table; querying a second relationship table according to the current noise standard deviation and the current input coefficient to obtain a current slope corresponding to the current input coefficient; generating a current output coefficient by multiplying the current input coefficient and a compression magnification function; and calculating the current output coefficient by substituting the current noise standard deviation, the current input coefficient, and the current slope.