Abstract: An encryption and signature device for AI model protection is provided. The encryption and signature device for AI model protection includes a key derivation unit, a model encryption unit, a model password encryption unit, an image generation unit and a signature unit. The key derivation unit is configured to derive a model key according to a model password and a derivation function. The model encryption unit is configured to encrypt an AI model according to the model key to generate an encrypted AI model. The model password encryption unit is configured to encrypt the model password to generate an encrypted model password. The image generation unit is configured to generate an image file according to the encrypted model password and the encrypted AI model. The signature unit is configured to sign the image file according to a private key to obtain a signed image file.

Abstract: An image processing method includes: (a) receiving a to-be-processed image; (b) obtaining a target brightness of the to-be-processed image; (c) creating a histogram curve of the to-be-processed image; (d) clipping the histogram curve according to a clipping value; (e) obtaining a mapping function of the clipped histogram curve; (f) obtaining a mapped image mapped according to the mapping function; (g) obtaining an average brightness of the mapped image; (h) obtaining a difference between the target brightness and the average brightness; and (i) if the difference is greater than a threshold, adjusting the clipping value and repeating steps (d) to (i) until the difference is less than the threshold. Thus, if the to-be-processed image does not reach the target brightness, the image processing device automatically repeats steps (c) to (i) until the to-be-processed image reaches the target brightness.

Abstract: An image processing method includes the following steps. Firstly, a block-size value is obtained. Then, a first frame into a plurality of first blocks according to the block-size value is divided. Then, a second frame into a plurality of second blocks according to the block-size value is divided. Then, a noise reduction intensity array is obtained according to a first pixel information of each first block and a second pixel information of each second block. Then, an output frame is obtained according to the noise reduction intensity array, the first frame and the second frame.

Abstract: The present invention discloses an image processing method. The image processing method includes the following steps: (a), a to-be-processed image is corrected as a first correction image according to a first mapping relationship along a correction direction; (b) the first correction image by an angle is rotated; and (c) the rotated first correction image is corrected as a second correction image according to a second mapping relationship along the same correction direction. In embodiment, given that the to-be-processed image is deformed along two different directions, the to-be-processed image is corrected along the same correction direction, such that correction complexity could be reduced.

Abstract: A mixed-precision artificial intelligence (AI) processor and an operating method thereof are provided. The AI processor includes a first calculation module, a second calculation module and a control module. The first calculation module is configured to perform calculation based on the data with a first format. The second calculation module is configured to perform calculation based on the data with a second format different from the first format. The control module is coupled to the first calculation module and the second calculation module to select one of the first calculation module or the second calculation module to perform calculation based on an input data according to a calculation strategy.

Abstract: An image processing method includes: (a) receiving a to-be-processed image; (b) obtaining a target brightness of the to-be-processed image; (c) creating a histogram curve of the to-be-processed image; (d) clipping the histogram curve according to a clipping value; (e) obtaining a mapping function of the clipped histogram curve; (f) obtaining a mapped image mapped according to the mapping function; (g) obtaining an average brightness of the mapped image; (h) obtaining a difference between the target brightness and the average brightness; and (i) if the difference is greater than a threshold, adjusting the clipping value and repeating steps (d) to (i) until the difference is less than the threshold. Thus, if the to-be-processed image does not reach the target brightness, the image processing device automatically repeats steps (c) to (i) until the to-be-processed image reaches the target brightness.

Abstract: An encryption and signature device for AI model protection is provided. The encryption and signature device for AI model protection includes a key derivation unit, a model encryption unit, a model password encryption unit, an image generation unit and a signature unit. The key derivation unit is configured to derive a model key according to a model password and a derivation function. The model encryption unit is configured to encrypt an AI model according to the model key to generate an encrypted AI model. The model password encryption unit is configured to encrypt the model password to generate an encrypted model password. The image generation unit is configured to generate an image file according to the encrypted model password and the encrypted AI model. The signature unit is configured to sign the image file according to a private key to obtain a signed image file.

Abstract: A computing device for floating-point mathematic operation using look-up table is provided. The computing device includes: a bit arrangement unit used for receiving a floating-point input data and performing a bit arrangement or a format conversion on the floating-point input data to generate multiple index blocks; a first look-up table unit group used for receiving the index blocks and performing look-up operation using the index blocks as index to generate a plurality of look-up table results; and an operation unit used for performing operation on the look-up table results of the first look-up table unit group to generate an operation output.

Abstract: A distorted image correcting method is applied to a distorted image correcting apparatus to correct a distorted image to further generate a corrected image. The image correcting apparatus includes a buffer, which includes a memory block having a capacity that is smaller than a size of a data block of the distorted image. The distorted image correcting method includes: controlling a memory controller to retrieve M sets of segment data of the data block to the memory block, where M is smaller than N; and generating a part of the corrected image according to the M sets of segment data.

Abstract: A distorted image correcting apparatus corrects a distorted image to generate a corrected image. The distorted image correcting apparatus includes: a buffer, including a plurality of memory blocks each storing one of a plurality of blocks of data of the distorted image; an allocation circuit, allocating the memory blocks in the buffer according to a memory configuration, wherein the memory blocks have different memory capacities; a memory controller, retrieving the blocks of data of the distorted image from a memory and storing the blocks of data to the buffer according to the memory configuration; and a correction circuit, retrieving a block of data from a memory block of the buffer, and generating a part of the corrected image according to the block of data.

Abstract: A multi-core electronic system for accessing a data storage device includes a plurality of processors, a data transmission interface and a rate adjustment module. The processors respectively provide a bandwidth requirement, and communicate with the data storage device via the shared data transmission interface. The rate adjustment module receives the bandwidth requirements, and determines a transmission rate of the data transmission interface according to the bandwidth requirements.

Abstract: A Hadamard transform-based image compression method includes performing a Hadamard transform on 2k pixel values according to a product of a 2k×2k stage Hadamard matrix and a 2k×2k adjustment matrix to generate 2k conversion values, where k is a positive integer and at least one of the 2k conversion values is zero. The adjustment matrix satisfies a condition that: when the 2k pixel values are divided into G pixel groups each comprising 2k/G adjacent pixels values and the adjustment matrix is multiplied with a first 2k×1 matrix formed by the 2k pixel values to transform the first 2k×1 matrix to a second 2k×1 matrix, each pixel value of the first matrix is transformed to an average of a pixel group comprising the pixel value to form the second 2k×1 matrix.

Abstract: An image processing method includes receiving an image data including a first pixel, a second pixel and a third pixel, the second pixel being between the first pixel and the third pixel; calculating a difference between two initial chrominance values of the first pixel and two initial chrominance values of the second pixel to determine a first difference, and calculating a difference between the two initial chrominance values of the second pixel and two initial chrominance values of the third pixel to determine a second difference; comparing the first difference with the second difference to select either the first pixel or the third pixel as a target pixel; and determining two adjusted chrominance values of the second pixel according to at least two initial chrominance values of the target pixel.

Abstract: An image processing apparatus comprises an image decoding unit and a static noise reducing unit. The image decoding unit decodes a data stream and generates a plurality of image comprising a current image. The static noise reducing unit, coupled to the image decoding unit, generates a first adjustment value of a target pixel of the current image according to an original luminance value of the target pixel and at least one original luminance value of a neighboring pixel associated with the target pixel. The static noise reducing unit also determines a static adjustment luminance value of the target pixel according to the original luminance value, the first adjustment value, and a first weight. The first weight is associated with the chrominance value of the target pixel.

Abstract: A television (TV) image adjusting method includes performing a variance calculation on luminance values of a plurality of pixels adjacent to target pixels of an image frame to generate a weight index; performing a decay function calculation according to the weight index to generate a weight distribution corresponding to the plurality of pixels adjacent to the target pixels. A function value of the decay function is inversely proportional to both the weight index and a target pixel distance between the target pixels and the plurality of pixels adjacent to the target pixels. The target pixel distance is a sum of vertical distances and horizontal distances between the target pixels and the plurality of pixels adjacent of the target pixels. The method further performs a weight calculation on the luminance values of the plurality of pixels adjacent to target pixels according to the weight distribution to generate an output luminance value.

Abstract: A multi-core electronic system for accessing a data storage device includes a plurality of processors, a data transmission interface and a rate adjustment module. The processors respectively provide a bandwidth requirement, and communicate with the data storage device via the shared data transmission interface. The rate adjustment module receives the bandwidth requirements, and determines a transmission rate of the data transmission interface according to the bandwidth requirements.

Abstract: An image processing apparatus includes an edge keeping index (EKI) generating unit and a noise reducing unit. The image decoding unit decodes a data stream to generate a plurality of image comprising at least a current image having the target pixel.

Abstract: An image processing apparatus includes an image decoding unit and an image adjusting unit. The image decoding unit decodes a data stream to generate a first image comprising a first pixel. The image adjusting unit comprises a pixel edge determining module and a chrominance adjusting module. The pixel edge determining module determines an edge luminance parameter according to an initial luminance value of the first pixel and a plurality of initial luminance values of pixels neighboring the first pixel, and determines an edge chrominance parameter according to an initial chrominance value of the first pixel and a plurality of initial chrominance values of pixels neighboring the first pixel. The first chrominance adjusting module determines a first weight according to the edge luminance parameter and the edge chrominance parameter, and generates a first adjusted chrominance value by weighted calculating a first predetermined chrominance value and the initial chrominance value of the first pixel.

Abstract: An image processing method includes generating a block matching result by performing a block matching operation according to a plurality of image blocks in the horizontal direction without referring to an image block in the vertical or other directions and performing an image processing operation according to the block matching result of the block matching operation.

Abstract: An image processing method and an image processing apparatus are provided. After receiving an image signal, the image and apparatus according to the invention first judge whether a target block in the image signal includes a non-chroma line. If the judging result is YES, a first edge detection procedure will be performed on the non-chroma line. If the judging result is NO, a second edge detection procedure will be performed on the target block.