case study embedded system digital camera

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Digital Camera Design An Interesting Case Study 1 Overview

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Computers & Mathematics With Applications

Tsutomu Shohdohji

The DCT (Discrete Cosine Transform) based coding process of full color images is standardized by the JPEG (Joint Photographic Expert Group). The JPEG method is applied widely, for example a color facsimile. The quantization table in the JPEG coding influences image quality. However, detailed research is not accomplished sufficiently about a quantization table. Therefore, we study the relations between quantization table and image quality. We examine first the influence to image quality given by quantization table. Quantization table is grouped into four bands by frequency. When each value of bands is changed, the merit and demerit of color image are examined. At the present time, we analyze the deterioration component of a color image. We study the relationship between the quantization table and the restoration image. Color image is composed of continuoustone level and we evaluate the deterioration component visually. We also analyze it numerically. An analysis method using the 2-D FFT (Fast Fourier Transform) can catch a change of a color image data by a quantization table change. On the basis of these results, we propose a quantization table using Fibonacci numbers.

surbhi singh , IJISRT digital library

Image compression is considered as application performed for compression of data in digital format images. Digital images are comprised with large amount of information that requires bigger bandwidth. The techniques of image compression can be generally categorized into two types: lossless & lossy technique. DCT (discrete cosine transform) can also be used for compressing an image and also approaches like Huffman encoding, quantization & such steps are required for compression of images with JPEG format. The format of JPEG can be used for both of the RGB (colored) & YUV (gray scale) images. But here our main concentration is over decompression & compression of gray scale images. Here 2D-DCT can be used for transformation of a 8x8 matrix of images to an elementary frequency elements. The DCT is considered to be a mathematical function which will transform an image of digital format from spatial to frequency domain. It is very much easy to implement Huffman encoding & decoding for minimizing the complexity of memory. In this proposed technique, the analog image pixels are transformed to discrete image pixel, and therefore compression is preformed. On the receiving side, the pixels are decompressed for obtaining the actual image. The PSNR is computed for analyzing the quality of image.

Werner Purgathofer

Omar Elgendy

Since the birth of charge coupled devices (CCD) and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors, pixel pitch of digital image sensors has been continuously shrinking to meet the resolution and size requirements of the cameras. However, shrinking pixels reduces the maximum number of photons a sensor can hold, a phenomenon broadly known as the full-well capacity limit. The drop in full-well capacity causes drop in signal-to-noise ratio and dynamic range.The Quanta Image Sensor (QIS) is a class of solid-state image sensors proposed by Eric Fossum in 2005 as a potential solution for the limited full-well capacity problem. QIS is envisioned to be the next generation image sensor after CCD and CMOS since it enables sub-diffraction-limit pixels without the inherited problems of pixel shrinking. Equipped with a massive number of detectors that have single-photon sensitivity, the sensor counts the incoming photons and triggers a binary response “1” if the photon c...

Optics, Photonics, and Digital Technologies for Multimedia Applications

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6-Performance Analysis of Embedded System Designs: Digital Camera Case Study

Aug 22, 2014

190 likes | 444 Views

6-Performance Analysis of Embedded System Designs: Digital Camera Case Study. Outline. Introduction to a simple digital camera Designer’s perspective Requirements specification Design Four implementations. Introduction. Putting it all together General-purpose processor

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• charge value
• average compression ratio
• discrete cosine transform

Presentation Transcript

Outline • Introduction to a simple digital camera • Designer’s perspective • Requirements specification • Design • Four implementations

Introduction • Putting it all together • General-purpose processor • Single-purpose processor • Custom • Standard • Memory • Interfacing • Knowledge applied to designing a simple digital camera • General-purpose vs. single-purpose processors • Partitioning of functionality among different processor types

Introduction to a simple digital camera • Captures images • Stores images in digital format • No film • Multiple images stored in camera • Number depends on amount of memory and bits used per image • Downloads images to PC • Only recently possible • Systems-on-a-chip • Multiple processors and memories on one IC • High-capacity flash memory • Very simple description used for example • Many more features with real digital camera • Variable size images, image deletion, digital stretching, zooming in and out, etc.

Designer’s perspective • Two key tasks • Processing images and storing in memory • When shutter pressed: • Image captured • Converted to digital form by charge-coupled device (CCD) • Compressed and archived in internal memory • Uploading images to PC • Digital camera attached to PC • Special software commands camera to transmit archived images serially

When exposed to light, each cell becomes electrically charged. This charge can then be converted to a 8-bit value where 0 represents no exposure while 255 represents very intense exposure of that cell to light. Some of the columns are covered with a black strip of paint. The light-intensity of these pixels is used for zero-bias adjustments of all the cells. The electromechanical shutter is activated to expose the cells to light for a brief moment. The electronic circuitry, when commanded, discharges the cells, activates the electromechanical shutter, and then reads the 8-bit charge value of each cell. These values can be clocked out of the CCD by external logic through a standard parallel bus interface. Lens area Covered columns Electro-mechanical shutter Electronic circuitry Pixel rows Pixel columns Charge-coupled device (CCD) • Special sensor that captures an image • Light-sensitive silicon solid-state device composed of many cells

Covered cells Zero-bias adjustment Before zero-bias adjustment After zero-bias adjustment Zero-bias error • Manufacturing errors cause cells to measure slightly above or below actual light intensity • Error typically same across columns, but different across rows • Some of left most columns blocked by black paint to detect zero-bias error • Reading of other than 0 in blocked cells is zero-bias error • Each row is corrected by subtracting the average error found in blocked cells for that row

Compression • Store more images • Transmit image to PC in less time • JPEG (Joint Photographic Experts Group) • Popular standard format for representing digital images in a compressed form • Provides for a number of different modes of operation • Mode used in this chapter provides high compression ratios using DCT (discrete cosine transform) • Image data divided into blocks of 8 x 8 pixels • 3 steps performed on each block • DCT • Quantization • Huffman encoding

DCT step • Transforms original 8 x 8 block into a cosine-frequency domain • Upper-left corner values represent more of the essence of the image • Lower-right corner values represent finer details • Can reduce precision of these values and retain reasonable image quality • FDCT (Forward DCT) formula • C(h) = if (h == 0) then 1/sqrt(2) else 1.0 • Auxiliary function used in main function F(u,v) • F(u,v) = ¼ x C(u) x C(v) Σx=0..7 Σy=0..7 Dxy x cos(π(2x+ 1)u/16) x cos(π(2y + 1)v/16) • Gives encoded pixel at row u, column v • Dxy is original pixel value at row x, column y • IDCT (Inverse DCT) • Reverses process to obtain original block (not needed for this design)

Quantization step • Achieve high compression ratio by reducing image quality • Reduce bit precision of encoded data • Fewer bits needed for encoding • One way is to divide all values by a factor of 2 • Simple right shifts can do this • Dequantization would reverse process for decompression Divide each cell’s value by 8 After being decoded using DCT After quantization

Huffman encoding step • Serialize 8 x 8 block of pixels • Values are converted into single list using zigzag pattern • Perform Huffman encoding • More frequently occurring pixels assigned short binary code • Longer binary codes left for less frequently occurring pixels • Each pixel in serial list converted to Huffman encoded values • Much shorter list, thus compression

Huffman codes Pixel frequencies Huffman tree 64 35 29 17 18 14 15 -1 11 10 9 6 8 5 -2 0 1 6 5 4 5 5 5 2 3 5 2 2 2 4 3 2 -5 -3 -10 1 1 1 1 1 1 -9 144 -4 -8 14 6 Huffman encoding example • Pixel frequencies on left • Pixel value –1 occurs 15 times • Pixel value 14 occurs 1 time • Build Huffman tree from bottom up • Create one leaf node for each pixel value and assign frequency as node’s value • Create an internal node by joining any two nodes whose sum is a minimal value • This sum is internal nodes value • Repeat until complete binary tree • Traverse tree from root to leaf to obtain binary code for leaf’s pixel value • Append 0 for left traversal, 1 for right traversal • Huffman encoding is reversible • No code is a prefix of another code

Archive step • Record starting address and image size • Can use linked list • One possible way to archive images • If max number of images archived is N: • Set aside memory for N addresses and N image-size variables • Keep a counter for location of next available address • Initialize addresses and image-size variables to 0 • Set global memory address to N x 4 • Assuming addresses, image-size variables occupy N x 4 bytes • First image archived starting at address N x 4 • Global memory address updated to N x 4 + (compressed image size) • Memory requirement based on N, image size, and average compression ratio

Uploading to PC • When connected to PC and upload command received • Read images from memory • Transmit serially using UART • While transmitting • Reset pointers, image-size variables and global memory pointer accordingly

Requirements Specification • System’s requirements – what system should do • Nonfunctional requirements • Constraints on design metrics (e.g., “should use 0.001 watt or less”) • Functional requirements • System’s behavior (e.g., “output X should be input Y times 2”) • Initial specification may be very general and come from marketing dept. • E.g., short document detailing market need for a low-end digital camera that: • captures and stores at least 50 low-res images and uploads to PC, • costs around $100 with single medium-size IC costing less that $25, • has long as possible battery life, • has expected sales volume of 200,000 if market entry < 6 months, • 100,000 if between 6 and 12 months, • insignificant sales beyond 12 months

Nonfunctional requirements • Design metrics of importance based on initial specification • Performance: time required to process image • Size: number of elementary logic gates (2-input NAND gate) in IC • Power: measure of avg. electrical energy consumed while processing • Energy: battery lifetime (power x time) • Constrained metrics • Values must be below (sometimes above) certain threshold • Optimization metrics • Improved as much as possible to improve product • Metric can be both constrained and optimization

Nonfunctional requirements (cont.) • Performance • Must process image fast enough to be useful • 1 sec reasonable constraint • Slower would be annoying • Faster not necessary for low-end of market • Therefore, constrained metric • Size • Must use IC that fits in reasonably sized camera • Constrained and optimization metric • Constraint may be 200,000 gates, but smaller would be cheaper • Power • Must operate below certain temperature (cooling fan not possible) • Therefore, constrained metric • Energy • Reducing power or time reduces energy • Optimized metric: want battery to last as long as possible

Zero-bias adjust CCD input DCT Quantize yes Done? no Archive in memory More 8×8 blocks? yes no Transmit serially serial output e.g., 011010... Informal functional specification • Flowchart breaks functionality down into simpler functions • Each function’s details could then be described in English • Done earlier in chapter • Low quality image has resolution of 64 x 64 • Mapping functions to a particular processor type not done at this stage

CCD.C 101011010110101010010101101... CODEC.C CCDPP.C image file CNTRL.C 1010101010101010101010101010... UART.C output file Refined functional specification • Refine informal specification into one that can actually be executed • Can use C/C++ code to describe each function • Called system-level model, prototype, or simply model • Also is first implementation • Can provide insight into operations of system • Profiling can find computationally intensive functions • Can obtain sample output used to verify correctness of final implementation Executable model of digital camera

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