Accelerating Color Space Conversion Using CUDA-Enabled Graphic Processing Units

2013 ◽  
Vol 716 ◽  
pp. 505-509 ◽  
Author(s):  
Hang Jun Yang ◽  
Jian Wang ◽  
Xiao Yong Ji
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
Multicore Processors ◽  
Color Space ◽  
Graphics Hardware ◽  
Functional Requirements ◽  
Graphic Processing Units ◽  
Color Space Conversion ◽  
Novel Approach ◽  
Software Implementations

Color space conversion (CSC) is an important kernel in the area of image and video processing applications including video compression. CSC is a compute-intensive time-consuming operation that consumes up to 40% of processing time of a highly optimised decoder. Several hardware and software implementations for CSC have been found. Hardware implementations can achieve a higher performance compared with software-only solutions. However, the flexibility of software solutions is desirable for various functional requirements and faster time to market. Multicore processors, especially programmable GPUs, provide an opportunity to increase the performance of CSC by exploiting data parallelism. In this paper, we present a novel approach for efficient implementation of color space conversion. The proposed approach has been implemented and verified using computed unified device architecture (CUDA) on graphics hardware. Our experiments results show that the speedup of up to17×can been obtained.

A Comparative Study of Color Space Conversion on Homogeneous and Heterogeneous Multicore

2014 ◽  
Vol 519-520 ◽  
pp. 724-728
Author(s):  
Chen Chu ◽  
Jian Wang ◽  
Sen Ke Hou ◽  
Qi Lv ◽  
Guo Qiang Ma ◽  
...  
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
Processing Time ◽  
Multicore Processors ◽  
Color Space ◽  
Data Parallelism ◽  
Heterogeneous Multicore ◽  
Color Space Conversion ◽  
Image And Video Processing ◽  
Novel Approaches

Color space conversion (CSC) is an important kernel in the area of image and video processing applications including video compression. As a matrix math, this operation consumes up to 40% of processing time of a highly optimized decoder. Therefore, techniques which efficiently implement this conversion are desired. Multicore processors provide an opportunity to increase the performance of CSC by exploiting data parallelism. In this paper, we present three novel approaches for efficient implementation of color space conversion suitable for homogeneous and heterogeneous multicore. We compare the performance of color space conversion on a variety of platforms including OpenMP running on homogeneous multicore CPUs, CUDA with NVIDIA GPUs and OpenCL running on both NVIDIA and ATI GPUs. Our experimental results show that the speedup of3×, 17×and15×can been obtained, respectively.

An Efficient Genetic Encoding Scheme for Multiplierless Color Space Converter Design

2013 ◽  
Vol 284-287 ◽  
pp. 3015-3019
Author(s):  
Ching Yi Chen ◽  
Ching Han Chen ◽  
Chih Hao Ma ◽  
Po Yi Wu
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
High Speed ◽  
Color Space ◽  
Low Complexity ◽  
Evolvable Hardware ◽  
Low Area ◽  
Design And Synthesis ◽  
Ycbcr Color Space ◽  
Field Programmable

Color space conversion has become a very important role in image and video processing systems. To speed up some processing processes, many communication and multimedia video compression schemes use luminance-chrominance type color spaces, such as YCbCr or YUV, making a mechanism for converting between different formats necessary. Therefore, techniques which efficiently implement this conversion are desired. For the recent years, a new field of research called Evolvable Hardware (EHW) has emerged which combines aspects of evolutionary computation with hardware design and synthesis. It is a new scheme inspired by natural evolution, for automatic design of hardware systems. This paper presents a novel evolutionary approach for efficient implementation of a RGB to YCbCr color space converter suitable for Field Programmable Gate Array (FPGAs) and VLSI. In the proposed method, we use the genetic algorithm to automatically evolve the multiplierless architecture of the color space converter. The architecture employs only a few shift and addition operations to replace the complex multiplications. The experimental results represent that the performance of implemented architecture is good at RGB to YCbCr color space converting, and it also has the advantages of high-speed, low-complexity, and low-area.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

Efficient RGB to YCbCr Color Space Conversion for Embedded Application

2014 ◽  
Vol 543-547 ◽  
pp. 2873-2878
Author(s):  
Hui Yong Li ◽  
Hong Xu Jiang ◽  
Ping Zhang ◽  
Han Qing Li ◽  
Qian Cao
Keyword(s):  
Direct Method ◽  
Color Space ◽  
Video Data ◽  
Floating Point ◽  
Portable Devices ◽  
Embedded Processor ◽  
Color Space Conversion ◽  
Ycbcr Color Space ◽  
Original Approach ◽  
General Method

Modern embedded portable devices usually have to deal with large amounts of video data. Due to massive floating-point multiplications, the color space conversion is inefficient on the embedded processor. Considering the characteristics of RGB to YCbCr color space conversion, this paper proposed a strategy for truncated-based LUT Multiplier (T-LUT Multiplier). On this base, an original approach converting RGB to YCbCr is presented which employs the T-LUT Multiplier and the pipeline-based adder. Experimental results demonstrate that the proposed method can obtain maximum operating frequency of 358MHz, 3.5 times faster than the direct method. Furthermore, the power consumption is less than that of the general method approximately by 15%~27%.

An Innovation System for Building Manufacturing

2012 ◽  
Author(s):  
Flávio Craveiro ◽  
João Meneses de Matos ◽  
Helena Bártolo ◽  
Paulo Bártolo
Keyword(s):  
New Technologies ◽  
Innovation System ◽  
Efficient Solutions ◽  
Urban Environments ◽  
Functionally Graded ◽  
Functional Requirements ◽  
Geometric Information ◽  
Material Deposition ◽  
Novel Approach ◽  
Graded Material

Traditionally the construction sector is very conservative, risk averse and reluctant to adopt new technologies and ideas. The construction industry faces great challenges to develop more innovative and efficient solutions. In recent years, significant advances in technology and more sustainable urban environments has been creating numerous opportunities for innovation in automation. This paper proposes a new system based on extrusion-based technologies aiming at solving some limitations of current technologies to allow a more efficient building construction with organic forms and geometries, based on sustainable eco principles. This novel approach is described through a control deposition software. Current modeling techniques focus only on capturing the geometric information and cannot satisfy the requirements from modeling the components made of multi-heterogeneous materials. There is a great deal of interest in tailoring structures so the functional requirements can vary with location. The proposed functionally graded material deposition (FGM) system will allow a smooth variation of material properties to build up more efficient buildings regarding thermal, acoustic and structural conditions.

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