Altek Corporation

Chen-Hung Chan

Hong-Long Chou

Ming-Jiun Liaw

Hsin-Te Wang

A noise elimination method of an image sequence is described. During a color separation of a raw image data captured by an image capturing element, 3D filtering is integrated. First, the raw image data is converted into a gray-scaled full luma image, an interframe filtering process is performed to eliminate possible noises in the gray-scaled full luma image, and an interpolation process is performed with the raw image data. During the implementation, an adaptive frame average filtering process is also performed to obtain a preferred image filtering result through an appropriate filtering manner. Therefore, an adaptive interframe interpolation for eliminating noises is to prevent noises or artifacts generated by the noises from affecting the subsequent image processing.

Noise elimination method of image sequence

A compression method is used for processing an image having a plurality of pixels, in which an image width of the image is W. The compression method includes the steps of selecting N continuous pixels from the image, where N is a positive integer greater than or equal to 2 and is less than the image width W; performing differential pulse code modulation (DPCM) so as to obtain N non-negative differences corresponding to the N pixels according to values of the N pixels; calculating to obtain a coding parameter according to the N non-negative differences; and coding the N non-negative differences according to the coding parameter.

Lossless image compression method

An image compression method s used for processing a plurality of pixels of an image. The image compression method includes the steps of receiving N successive pixels; obtaining N pixel differences and N map differences corresponding to the N pixels according to values of the N pixels through differential pulse code modulation; calculating to obtain a quantization reference value corresponding to the N pixels according to the N pixel differences; obtaining a quantization parameter corresponding to the N pixels according to the quantization reference value; and encoding the N map differences according to the quantization parameter.

Image compression method with variable quantization parameter

An integer division circuit with allowable error is described, what a signal processing apparatus includes a pointer, a first left shifter, a second left shifter, a subtractor, a multiplier, and a right shifter. The pointer searches for a most significant non-zero bit of a divisor and outputs a most significant byte value. The first left shifter performs a shift operation according to the most significant byte value, so as to generate a first exponential coefficient. The second left shifter performs a shift operation according to the most significant byte value, so as to generate a second exponential coefficient. The subtractor calculates a multiplier factor according to the divisor, the first exponential coefficient, and the second exponential coefficient and outputs the multiplier factor to the multiplier. The multiplier multiplies an input value with the multiplier factor and outputs a result to the right shifter. The right shifter outputs a calculation result.

Integer division circuit with allowable error

A method for beautifying a human face in a digital image is adapted to beautify a face area of an input image. The method includes setting a selection window to select a partial image area in the input image; setting a target pixel in the selection window, and setting other pixels as comparison pixels; performing a detail checking process according to a variance between the target pixel; performing a luminance checking process on the target pixel to determine; performing a nonlinear filtering process to filter the target pixel by using a nonlinear filter to generate a filtered value, and providing a mixing ratio to mix the target pixel with the filtered value at the mixing ratio to generate a completed pixel; replacing the original target pixel with the completed pixel; and repeating the above steps until all pixels are completed.

Method for beautifying human face in digital image

A method of establishing a skin color model includes the following steps. A human face detecting procedure is performed on an input image, and a human face area in the input image is circled through a selecting window. A skin color model is established by using a Gaussian probability distribution function (PDF) according to color information in the selecting window. When the skin color model established in the above step is applied to skin color detection, pixels having the skin color in the input image are detected through the skin color model and a Mahalanobis distance computing procedure.

Method of establishing skin color model

An image compression method is used for processing a plurality of pixels of an image. The image compression method includes the steps of receiving N pixels; analyzing the N pixels and generating a content type corresponding to the N pixels; obtaining a quantization parameter and a coding parameter corresponding to the N pixels according to the content type and a currently available buffer space value; obtaining N quantized differences corresponding to the N pixels through a prediction and quantization way according to values of the N pixels and the quantization parameter; and encoding the N quantized differences according to the N quantized differences and the coding parameter.

Image compression method with variable quantization parameters and variable coding parameters

A method for adjusting a skin color of a digital image adjusts the skin color of an input image. The method includes performing a skin color detection process on the input image to generate a skin-color probability plot Sp in a size corresponding to the input image; providing a hue-saturation lookup table named LUT_Color; performing a skin-color reproduction process on the input image to look up the LUT_Color for a chrominance pixel value for each pixel value of the input image to generate a first image, and adjust each pixel value of the first image by using the skin-color probability plot Sp to generate a second image; performing a skin color smoothing process on the second image to generate a third image; and mixing pixel values of the input image and the third image to generate a target image.

Method for adjusting skin color of digital image

A color interpolation method for a digital image is described, includes the following steps. Edge information is generated according to the Bayer pattern. A Bayer pattern image is processed by a horizontal band-pass filter and a vertical band-pass filter to generate a horizontal edge signal and a vertical edge signal. A weight process implements a weight adjustment on the horizontal edge signal and the vertical edge signal to generate a luminance signal and outputs the luminance signal to a subtraction unit. The subtraction unit carries out an operation according to the Bayer pattern image and an output result of the weight process to generate a chrominance pattern. The chrominance pattern is compensated through a chrominance interpolation process, so as to generate image signal patterns of different colors. The image signal patterns of different colors are output to an adder for an add operation, thereby outputting a complete color image.

Color interpolation method for digital image

An image noise reduction method based on an optimal primary-color signal is suitable for a digital camera to reduce noises in a digital image. The image noise reduction method mainly includes the following steps, taking out primary-color signals of a digital image, including taking out high-frequency and low-frequency signals of the optimal primary-color signal and high-frequency and low-frequency signals of the worst primary-color signal by using a high-pass filter and a low-pass filter; next, mixing the high-frequency signal of the optimal primary-color signal with that of the worst primary-color signal, and superposing the mixed signal to the low-frequency signal of the worst primary-color signal, so as to get a digital image signal after noise reduction.

Image noise reduction method based on optimal primary-color signal

A dead pixel real-time detection method for image applicable in a digital camera is provided. The method is utilized to achieve the real-time detection of dead pixels in an image, upon obtaining the image by the digital camera, thus locating the dead pixel desired to be repaired. Through the real-time detection, the large amount of manpower and working-hours spent on detection and correction of the dead pixels of digital cameras on a production line can be saved, and the large amount of storage space occupied by the position information of the dead pixels in a digital camera can also be reduced. More importantly, in the detection process, the dead pixels can be determined more accurately through dynamically adjusting a threshold value as based on the different attributes of the various images, thus achieving the raising of the quality of the images significantly.

Growing community of inventors

Taoyuan County, Taiwan

Chen-Hung Chan