In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.

A Dynamic Histogram Equalization for Image Contrast Enhancement

Image inpainting refers to the process of restoring missing or damaged areas in an image. This field of research has been very active over recent years, boosted by numerous applications: restoring images from scratches or text overlays, loss concealment in a context of impaired image transmission, object removal in a context of editing, or disocclusion in image-based rendering (IBR) of viewpoints different from those captured by the cameras. Although earlier work dealing with disocclusion has been published in [1], the term inpainting first appeared in [2] by analogy with a process used in art restoration.

Image Inpainting : Overview and Recent Advances

A review of smart home applications based on Internet of Things

MPEG-4 is the multimedia standard for combining interactivity, natural and synthetic digital video, audio and computer-graphics Typical applications are: internet, video conferencing, mobile videophones, multimedia cooperative work, teleteaching and games With MPEG-4 the next step from block-based video (ISO/IEC MPEG-1, MPEG-2, CCITT H261, ITU-T H263) to arbitrarily-shaped visual objects is taken This significant step demands a new methodology for system analysis and design to meet the considerably higher flexibility of MPEG-4 Motion estimation is a central part of MPEG-1/2/4 and H261/H263 video compression standards and has attracted much attention in research and industry, for the following reasons: it is computationally the most demanding algorithm of a video encoder (about 60-80% of the total computation time), it has a high impact on the visual quality of a video encoder, and it is not standardized, thus being open to competition Algorithms, Complexity Analysis, and VLSI Architectures for MPEG-4 Motion Estimation covers in detail every single step in the design of a MPEG-1/2/4 or H261/H263 compliant video encoder: Fast motion estimation algorithms Complexity analysis tools Detailed complexity analysis of a software implementation of MPEG-4 video Complexity and visual quality analysis of fast motion estimation algorithms within MPEG-4 Design space on motion estimation VLSI architectures Detailed VLSI design examples of (1) a high throughput and (2) a low-power MPEG-4 motion estimator Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation is an important introduction to numerous algorithmic, architectural and system design aspects of the multimedia standard MPEG-4 As such, all researchers, students and practitioners working in image processing, video coding or system and VLSI design will find this book of interest

Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation

Neural Networks (NN) are a family of models for a broad range of emerging machine learning and pattern recondition applications. NN techniques are conventionally executed on general-purpose processors (such as CPU and GPGPU), which are usually not energy-efficient since they invest excessive hardware resources to flexibly support various workloads. Consequently, application-specific hardware accelerators for neural networks have been proposed recently to improve the energy-efficiency. However, such accelerators were designed for a small set of NN techniques sharing similar computational patterns, and they adopt complex and informative instructions (control signals) directly corresponding to high-level functional blocks of an NN (such as layers), or even an NN as a whole. Although straightforward and easy-to-implement for a limited set of similar NN techniques, the lack of agility in the instruction set prevents such accelerator designs from supporting a variety of different NN techniques with sufficient flexibility and efficiency.In this paper, we propose a novel domain-specific Instruction Set Architecture (ISA) for NN accelerators, called Cambricon, which is a load-store architecture that integrates scalar, vector, matrix, logical, data transfer, and control instructions, based on a comprehensive analysis of existing NN techniques. Our evaluation over a total of ten representative yet distinct NN techniques have demonstrated that Cambricon exhibits strong descriptive capacity over a broad range of NN techniques, and provides higher code density than general-purpose ISAs such as ×86, MIPS, and GPGPU. Compared to the latest state-of-the-art NN accelerator design DaDianNao [5] (which can only accommodate 3 types of NN techniques), our Cambricon-based accelerator prototype implemented in TSMC 65nm technology incurs only negligible latency/power/area overheads, with a versatile coverage of 10 different NN benchmarks.

Cambricon: an instruction set architecture for neural networks

In this paper, we propose a new scheme to recognize old-film scene and modern scene. The proposed method automatically divides a video into several scenes. An adaptive histogram transform is performed on frames to yield flicker-free frames, and intensity flicker is measured and defined as the difference between original frame and flicker-free frame. A quantitative measurement of intensity flicker is computed as feature. Moreover, using color feature and the results of scene recognition, the proposed method identifies the characteristic of video and estimates the era of film in three periods, including, early period, middle period, and modern period. The experiment results will demonstrate that the proposed method not only recognizes old-film scene and modern scene, but it can handle video identification and era estimation of film and video.

Scene recognition using quantitative measurement of intensity flicker

In this paper, we propose an integer-based pixel blurriness measure scheme. All values are formed as combination of an integer and a fraction. Three fixed-point operations are implemented, including fractional addition, fractional multiplication, and fractional division. Moreover, mean and variance are computed based on fixed-point operations. Pixel blurriness is defined as slope of pixels' difference over the captured image and four smoothed images, and it is capable of determining proper focus parameters for autofocus. The experiment results will demonstrate that our scheme has low precision loss of pixel blurriness, determines the best focus parameters, and achieves multiple applications.

Yi-Chong Zeng

Papers

Scene recognition using quantitative measurement of intensity flicker

Integer-based pixel blurriness measure and its applications