Product Family Features The i.MX6 series unleashes the industry’s first truly scalable multicore platform that includes single-, dual- and quad-core families based on the ARM® Cortex™-A9 architecture. Together with a robust ecosystem, i.MX6 series provides the ideal platform to develop a portfolio of end devices based on a single hardware design. With high-performance multimedia processing, pin*- and software- compatible product families and integrated power management, i.MX6 series is purpose built for the new era of smart devices. *4 of 5 families are pin-compatible The i.MX6 applications processor is a Energy-Efficient Solutions products. Automotive As drivers adopt personal and home-based smart devices, automotive manufacturers are bringing a similar experience in-vehicle. Able to meet demands of connectivity, real time data delivery, digital instrumentation, audio and multi-stream video, i.MX 6 series enables auto infotainment and instrument cluster designers to re-create today’s consumer technology experience in the car. Smart Devices The market for intelligent, multimedia centric, touch based devices is increasing exponentially. Not just for tablets or smartphones anymore, tomorrow's battery powered Smart Devices, Aero Infotainment systems, medical systems, enterprise-class intelligent control and data systems all must present data and user interface choices to the end user primarily through rich sound, video, voice, pictures and touch, rather than keyboards and mice. i.MX 6 series enables developers to deliver a more seamless natural user interface (NUI) experience, plus save time and costs by leveraging one design across a portfolio of devices. i.MX 6 Series Portfolio View the complete i.MX 6 Series; compare features and performance   Product Information i.MX6DL: i.MX 6DualLite Family of Applications Processors i.MX6S: i.MX 6Solo Family of Applications Processors i.MX6D: i.MX 6Dual Family of Applications Processors i.MX6Q: i.MX 6Quad Family of Applications Processors i.MX6SL: i.MX 6SoloLite Family of Applications Processors Design Resources i.MX 6 Series Software and Development Tools i.MX 6SoloLite Evaluation Kit SABRE Platform for Smart Devices SABRE Board for Smart Devices SABRE for Automotive Infotainment i.MX 6 Family Ecosystem Partners Partners / 3rd-Party Development Tools Development platform for i.MX 6Quad - Built to SABRE Lite design from Element 14 Element14's SabreLite Board Officially Supported by Adeneo Embedded's i.MX6 WEC7 BSP Emtrion's i.MX6 DIMM Modules and Kits i.Core M6 : i.Mx6 based SOM Industry-First Pico-ITX SBC based on i.MX6 from iWave Systems i.MX6 Q7 Development Kit by iWave Systems New PMIC to Support the i.MX6 Processor Family NovPek i.MX6Q/D by NovTech Video- iWave Launches Industry's first i.MX6 Solo/Dual Lite Based Pico-ITX Single Board Computer i.MX6 Q7 Development Kit by iWave Systems The Wandboard - ultra lowcost development board with i.MX6 Cortex-A9 processor SABRE Lite by Boundary Devices Nitrogen6X by Boundary Devices Additional Resources i.MX6 (All) Tips & Tricks Android data partition encryption on i.MX6 Android Graphic UI with GPU Hardware Acceleration Auto Insmod Kernel Modules Through Modprobe with Extra Parameter A Patch to Fix i.MX6 GPU Startup Issue Due to Memory Connection Qt Landing page De-interlace Capture Device Enabling MMU and Caches on i.MX6 Series Platform SDK Errata_ERR006282_Description_IMX_Community.pdf Fast GPU Image Processing in the i.MX 6x Freescale Yocto Project main page Gstreamer HW Design Checklist for i.MX6 How to Add Ethernet UI Support in ICS How to Support New WiFi Card in Android How to Support Recovery Mode for POR Reboot Based on i.MX6 Android R13.4.1 How to Trace the Low-Level Malloc i.MX6 Crystal Drive Level (24 MHz) EB830 i.MX6 Android 13.4.1.03 Patch Release i.MX6 Dual/6 Quad Power Consumption Measurement Scripts i.MX6 IPU Output Timing Generation Counters and Interrupts i.MX6 Platform SDK 1.1 Release i.MX6 VDD_SNVS_CAP Component Recommendation Linux Fast Boot on i.MX6 Sabresd Board LMbench Benchmarks on i.MX New PMIC to Support the i.MX6 Processor Family Memory Management on i.MX6 Android Patch to Support BT656 and BT1120 Output For i.MX6 BSP Prevent PMIC PF0100 Backfeed on i.MX6 Designs Using a USB Camera with GStreamer VAR-SOM-MX6, $52 i.MX6 System on Module i.MX6D/6Q (Dual/Quad) Tips & Tricks De-interlace Capture Device Android Power Management on i.MX6DQ/DL Android Graphic UI with GPU Hardware Acceleration Memory Management on i.MX6 Android iMX6QD How to add 24-bit LVDS support in Android i.MX6 D/Q L3.035_1.0.2 Patch Release i.MX6 D/Q L3.0.35_1.0.3 patch release i.MX6 D/Q L3.035_1.1.3 patch release i.MX6Q Ubuntu Fluxbox Multimedia with VPU & IPU HW Acceleration in Android Let Ubuntu NetworkManager Recognize BCM4330 Wireless Interface Auto Insmod Kernel Modules Through Modprobe with Extra Parameter Video Playback Performance Evaluation on i.MX6DQ Board Linux Fast Boot on i.MX6 Sabresd Board Linux Fast Boot on i.MX6Q Board: Building Steps New Ubuntu SD Card Demo Image for the i.MX6Q SABRE AI SDMA ap_to_ap Fixed Scripts (i.MX6DQ) Surround View Demo With Linux Fast Boot Review Surround View (D1) Demo on i.MX6 Test Digital Zoom of Camera Preview Using i.MX6Q to Build a Palm-Sized Heterogeneous Mini-HPC i.MX6DL (DualLite)  Tips & Tricks Android Power Management on i.MX6DQ/DL i.MX6 DL/S L3.035_3.0.4 patch release i.MX6SL (SoloLite)  Tips & Tricks Dithering Implementation for Eink Display Panel

Dithering Implementation for Eink Display Panel by Daiyu Ko, Freescale Dithering a.          Dithering in digital image processing Dithering is a technique used in computer graphics to create the illusion of color depth in images with a limited color palette (color quantization). In a dithered image, colors not available in the palette are approximated by a diffusion of colored pixels from within the available palette. The human eye perceives the diffusion as a mixture of the colors within it (see color vision). Dithered images, particularly those with relatively few colors, can often be distinguished by a characteristic graininess, or speckled appearance. Figure 1. Original photo; note the smoothness in the detail http://en.wikipedia.org/wiki/File:Dithering_example_undithered_web_palette.png Figure 2.Original image using the web-safe color palette with no dithering applied. Note the large flat areas and loss of detail. http://en.wikipedia.org/wiki/File:Dithering_example_dithered_web_palette.png Figure 3.Original image using the web-safe color palette with Floyd–Steinberg dithering. Note that even though the same palette is used, the application of dithering gives a better representation of the original b.         Applications Display hardware, including early computer video adapters and many modern LCDs used in mobile phonesand inexpensive digital cameras, show a much smaller color range than more advanced displays. One common application of dithering is to more accurately display graphics containing a greater range of colors than the hardware is capable of showing. For example, dithering might be used in order to display a photographic image containing millions of colors on video hardware that is only capable of showing 256 colors at a time. The 256 available colors would be used to generate a dithered approximation of the original image. Without dithering, the colors in the original image might simply be "rounded off" to the closest available color, resulting in a new image that is a poor representation of the original. Dithering takes advantage of the human eye's tendency to "mix" two colors in close proximity to one another. For Eink panel, since it is grayscale image only, we can use the dithering algorism to reduce the grayscale level even to black/white only but still get better visual results. c.          Algorithm There are several algorithms designed to perform dithering. One of the earliest, and still one of the most popular, is the Floyd–Steinberg dithering algorithm, developed in 1975. One of the strengths of this algorithm is that it minimizes visual artifacts through an error-diffusion process; error-diffusion algorithms typically produce images that more closely represent the original than simpler dithering algorithms. (Original) Threshold Bayer   (ordered)                                     Example (Error-diffusion): Error-diffusion dithering is a feedback process that diffuses the quantization error to neighboring pixels. Floyd–Steinberg dithering only diffuses the error to neighboring pixels. This results in very fine-grained dithering. Jarvis, Judice, and Ninke dithering diffuses the error also to pixels one step further away. The dithering is coarser, but has fewer visual artifacts. It is slower than Floyd–Steinberg dithering because it distributes errors among 12 nearby pixels instead of 4 nearby pixels for Floyd–Steinberg. Stucki dithering is based on the above, but is slightly faster. Its output tends to be clean and sharp. Floyd–Steinberg Jarvis,   Judice & Ninke Stucki                         Error-diffusion dithering (continued): Sierra dithering is based on Jarvis dithering, but it's faster while giving similar results. Filter Lite is an algorithm by Sierra that is much simpler and faster than Floyd–Steinberg, while still yielding similar (according to Sierra, better) results. Atkinson dithering, developed by Apple programmer Bill Atkinson, resembles Jarvis dithering and Sierra dithering, but it's faster. Another difference is that it doesn't diffuse the entire quantization error, but only three quarters. It tends to preserve detail well, but very light and dark areas may appear blown out. Sierra Sierra   Lite Atkinson                              2.     Eink display panel characteristic a.       Low resolution Eink only has couple resolution modes for display      DU                  (1bit, Black/White)      GC4                (2bit, Gray scale)      GC16              (4bit, Gray scale)      A2                   (1bit, Black/White, fast update mode) b.      Slow update time For 800x600 panel size (per frame)      DU                  300ms                              GC4                450ms                              GC16              600ms                               A2                   125ms 3.       3.     Effect by doing dithering for Eink display panel a.       Low resolution with better visual quality By doing dithering to the original grayscale image, we can get better visual looking result. Even if the image becomes black and white image, with the dithering algorism, you will still get the feeling of grayscale image. b.      Faster update with Eink’s animation waveform Since the DU/A2 mode could update the Eink panel faster than grayscale mode, with dithering, we can get no only the better visual looking result, but also we can use DU/A2 fast update mode to show animation or even normal video files. 4.       4.     Our current dithering implementation a.       Choose a simple and effective algorism Considering Eink panel’s characteristics, we compared couple dithering algorism and decide to use Atkinson dithering algorism. It is simple and the result is better especially for Einkblack/white display case. b.      Made a lot of optimization so that it will not affect update time too much With the simplicity of the Atkinson dithering algorism, we can also put a lot of effort to do the optimization in order to reduce the dithering processing time and make it practical for actual use. c.       Current algorism performance and result Currently, with Atkinson dithering algorism, our processing time is about 70ms. 5.       5.     Availability a.       We implemented both Y8->Y1 and Y8->Y4 dithering with the same dithering algorism. b.      Implemented into our EPDC driver with i.MX6SL Linux 3.0.35 version release. c.       Also implemented in our Video for Eink demo 6.       6.     References a.       Part of dithering introduction from www.wikipedia.org