This application note covers the design considerations of a system using the performance
features of the LogiCORE™ IP Advanced eXtensible Interface (AXI) Interconnect core. The
design focuses on high system throughput through the AXI Interconnect core with F
MAX
and
area optimizations in certain portions of the design.
The design uses five AXI video direct memory access (VDMA) engines to simultaneously move
10 streams (five transmit video streams and five receive video streams), each in 1920 x 1080p
format, 60 Hz refresh rate, and up to 32 data bits per Pixel. Each VDMA is driven from a video
test pattern generator (TPG) with a video timing controller (VTC) block to set up the necessary
video timing signals. Data read by each AXI VDMA is sent to a common on-screen display
(OSD) core capable of multiplexing or overlaying multiple video streams to a single output video
stream. The output of the OSD core drives the DVI video display interface on the board.
Performance monitor blocks are added to capture performance data. All 10 video streams
moved by the AXI VDMA blocks are buffered through a shared DDR3 SDRAM memory and are
controlled by a MicroBlaze™ processor.
The reference system is targeted for the Virtex-6 XC6VLX240TFF1156-1 FPGA on the
Xilinx® ML605 Rev D evaluation board
This application note covers the design considerations of a system using the performance
features of the LogiCORE™ IP Advanced eXtensible Interface (AXI) Interconnect core. The
design focuses on high system throughput through the AXI Interconnect core with F
MAX
and
area optimizations in certain portions of the design.
The design uses five AXI video direct memory access (VDMA) engines to simultaneously move
10 streams (five transmit video streams and five receive video streams), each in 1920 x 1080p
format, 60 Hz refresh rate, and up to 32 data bits per Pixel. Each VDMA is driven from a video
test pattern generator (TPG) with a video timing controller (VTC) block to set up the necessary
video timing signals. Data read by each AXI VDMA is sent to a common on-screen display
(OSD) core capable of multiplexing or overlaying multiple video streams to a single output video
stream. The output of the OSD core drives the DVI video display interface on the board.
Performance monitor blocks are added to capture performance data. All 10 video streams
moved by the AXI VDMA blocks are buffered through a shared DDR3 SDRAM memory and are
controlled by a MicroBlaze™ processor.
The reference system is targeted for the Virtex-6 XC6VLX240TFF1156-1 FPGA on the
Xilinx® ML605 Rev D evaluation board
The Hopfield model is a distributed model of an associative memory. Neurons are Pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of Pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
Wavelets have widely been used in many signal and image processing applications. In this paper, a new
serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet
transform, which is realised using some FIFO memory modules implementing a Pixel-level pipeline
architecture to compress and decompress images. The real filter calculation over 4 · 4 window blocks is
done using a tree of carry save adders to ensure the high speed processing required for many applications.
The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis
reveals that the proposed architecture, implemented using current VLSI technologies, can process a
video stream in real time.
In
this paper we propose to reduce the textural components by
modelling the coefficients of a wedgelet based regression tree
instead of the original Pixel intensities
The problem of image registration subsumes a number of problems and techniques in multiframe
image analysis, including the computation of optic flow (general Pixel-based motion), stereo
correspondence, structure from motion, and feature tracking. We present a new registration
algorithm based on spline representations of the displacement field which can be specialized to
solve all of the above mentioned problems. In particular, we show how to compute local flow,
global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of
the above problems. Using a spline-based description of the flow removes the need for overlapping
correlation windows, and produces an explicit measure of the correlation between adjacent flow
estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D
projective scene geometry. We also provide results on a number of standard motion sequences.
map identification laquo yuan Problem description : the digital image processing of an image often expressed as a macute M Pixel matrix. Each Pixel value is 0 or 1. The value of 0 Pixel image, said background, and the value of a Pixel image, said a map of a million, usually called map million Pixels. When a Pixel in another Pixels above, below, left or right, said that two adjacent Pixels of the Pixel. An image of the Pixels belonging to the same map yuan, instead of adjacent Pixels belonging to different map yuan. Figure yuan identification of problems is right for the given image Pixel map marking yuan, making the same map billion yuan Pixel map of the same markings and different map billion yuan Pixel map of their marks are different. Trial queue abstract data type design solut
When working with mathematical simulations or engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same Pixel since the screen precision is finite. Hence, you use a lot of resource for nothing!
This article presents a fast 2D-line approximation algorithm based on the Douglas-Peucker algorithm (see [1]), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages:
這是一個基于Douglas-Peucker算法的二維估值算法。
