Deep neural network (NN) has been widely accepted for enabling various AI applications, however, the limitation of computational and memory resources is a major problem on mobile devices. Quantized NN with a reduced bit precision is an effective solution, which relaxes the resource requirements, but the accuracy degradation due to its numerical approximation is another problem. We propose a novel quantized NN model employing the “dithering” technique to improve the accuracy with the minimal additional hardware requirement at the view point of the hardware-algorithm co-designing. Dithering distributes the quantization error occurring at each pixel (neuron) spatially so that the total information loss of the plane would be minimized. The experiment we conducted using the software-based accuracy evaluation and FPGA-based hardware resource estimation proved the effectiveness and efficiency of the concept of an NN model with dithering.

In this paper, we propose a method for color error diffusion based on the Neugebauer model for color halftone printing. The Neugebauer model expresses an arbitrary color as a trilinear interpolation of basic colors. The proposed method quantizes the color of each pixel to a basic color which minimizes an accumulated quantization error, and the quantization error is diffused to the ratios of basic colors in subsequent pixels. Experimental results show that the proposed method outperforms conventional color error diffusion methods including separable method in terms of eye model-based mean squared error.

The error diffusion filter in this paper is optimized with respect to the ideal blue noise pattern corresponding to a single tone level. The filter coefficients are optimized by the minimization of the squared error norm between the Fourier power spectra of the resulting halftone and the blue noise pattern. During the process of optimization, the binary pattern power spectrum matching algorithm is applied with the aid of a new blue noise model. The number of the optimum filters is equal to that of different tones. The visual fidelity of the bilevel halftones generated by the error diffusion filters is evaluated in terms of a weighted signal-to-noise ratio, Fourier power spectra, and others. Experimental results have demonstrated that the proposed filter set generates satisfactory bilevel halftones of grayscale images.

The reduction of a structural pattern at specific gray levels or at the special condition of image data has mainly been discussed in digital halftone methods. This problem is more severe in some flat panel displays because their black levels typically are brighter than other displays blocks. The authors proposed an advanced confined error diffusion (ACED) algorithm which was a well-organized halftone algorithm for flat panel devices. In this paper, we extend the ACED algorithm to the multi-level systems, which are capable of displaying more than 2 levels. Our extension has two merits for the hardware implementation. First, it can be processed in real time using the look-up table based method. The second one is the flexibility of selecting the used gray level. This paper discusses the performance of the proposed algorithms with experimental results for natural test images.

A 3 dimensional (3D) error diffusion method based on edge detection for flat panel display (FPD) is presented. The new method diffuses errors to the neighbor pixels in current frame and the neighbor pixel in the next frame. And the weights of error filters are dynamically adjusted based on the results of edge detection in each pixel's processing, which makes the weights coincide with the local edge feathers of input image. The proposed method can reduce worm artifacts and improve reproduction precision of image details.

A triple density Error Diffusion for medical monochrome LCDs is proposed to improve their gray-scale precisions. In addition, a measurement technique of image qualities based on E-MSE (Eye model-based Mean Square Error) is proposed. Several conventional techniques for medical LCDs, such as Sub-Pixel Modulation and Error Diffusion, are evaluated based on E-MSE and the validity of the proposed technique is ensured objectively.

Digital halftoning is used to quantize a grayscale image to a binary image. Error diffusion halftoning generates a high-quality binary image, but also generates some defects such as the warm effect, sharpening, and so forth. To reduce these defects, Kite proposed a modified threshold modulation method that utilizes a multiplicative parameter for controlling sharpening. Nevertheless, some degradation was observed near the edges of objects with a large luminance change. In this paper, we propose a method of controlling the multiplicative parameter in proportion to the magnitude of the local edge slope. The results of computer simulation show a greater reduction of sharpening in the halftone image. In particular, there is a great improvement in the quality of the edges of objects with a large luminance change.

This letter introduces a new digital halftoning technique based on error diffusion along a random space-filling curve. The purpose of introducing randomness is to erase regular patterns which tend to arise in an image area of uniform intensity. A simple algorithm for generating a random space-filling curve is proposed based on a random spanning tree and maze traversal. Some experimental results are also given.

We propose a new method of progressive transmission of continuous tone images using multi-level error diffusion method. Assuming that the pixels are ordered and the error is diffused to later pixels, multi-level error-diffused images are resolved into a multiple number of bit planes. In an image with 8 bits per pixel, the number of the bit planes that we construct is 9, and the 2-level, 3-level, 5-level,, error-diffused images are produced by a successive use of the bit planes. The original image is finally achieved precisely.