Whiteboards support face to face meetings by facilitating the sharing of ideas, focusing attention, and summarizing.
However, at the end of the meeting participants desire some record of the information from the whiteboard. While there
are whiteboards with built-in printers, they are expensive and relatively uncommon. We consider the capture of the
information on a whiteboard with a mobile phone, improving the image quality with a cloud service, and sharing the
results. This paper describes the algorithm for improving whiteboard image quality, the user experience for both a web
widget and a smartphone application, and the necessary adaptations for providing this as a web service. The web widget,
and mobile apps for both iPhone and Android are currently freely available, and have been used by more than 50,000
This paper reports on novel and traditional pixel and semantic operations using a recently standardized document representation called JPM. The JPM representation uses compressed pixel arrays for all visible elements on a page. Separate data containers called boxes provide the layout and additional semantic information. JPM and related image-based document representation standards were designed to obtain the most rate efficient document compression. The authors, however, use this representation directly for operations other than compression typically performed either on pixel arrays or semantic forms. This paper describes the image representation used in the JPM standard and presents techniques to (1) perform traditional raster-based document analysis on the compressed data, (2) transmit semantically meaningful portions of compressed data between devices, (3) create multiple views from one compressed data stream, and (4) edit high resolution document images with only low resolution proxy images.
In this paper, a novel method is proposed to locate one-dimensional barcodes in JPEG 2000 images. The barcode
locating system consists of two parts: candidate barcode location detection, barcode location verification and
refinement. Both parts are designed to work in the compressed domain of JPEG 2000 images. The locations of
candidate barcodes are extracted from the header data and verified by examining part of the decoded coefficients of the
JPEG 2000 file. Since only a small amount of the compressed data is used, this algorithm has a low complexity relative
to algorithms which use all of the pixel data.
The development of the JPEG 2000 standard was quite contentious due to the widely varying goals of the participants. The result was that JPEG 2000 is the most flexible image compression standard to date. However, in order to complete the standard within the 2000 calendar year any definition of what a decoder must do was removed from the initial standard. JPEG 2000 Part 4 is the standard which defines conformance of a decoder (and to a lessor extent an encoder) with JPEG 2000. This paper describes how decisions were made about compliance with JPEG 2000 and the methods used for testing.
The JPEG 2000 image compression system offers significant opportunity to improve imaging over the Internet. The JPEG 2000 standard is ideally suited to the client/server architecture of the web. With only one compressed version stored, a server can transmit an image with the resolution, quality, size, and region custom specified by an individual client. It can also serve an interactive zoom and pan client application. All of these can be achieved without server side decoding while using only minimal server computation, storage, and bandwidth. This paper discusses some of the system issues involved in Internet imaging with JPEG 2000. The choices of the client, passing of control information, and the methods a server could use to serve the client requests are presented. These issues include use of JPEG 2000 encoding and the decoding options in the standard. Also, covered are some proposed techniques that are outside the existing standards.
The JPEG2000 still image compression standard has adopted a new compression paradigm: 'Encode once, decode many.' JPEG2000 architecture allows the codestream to be decoded differently for different application needs. The codestream can be decoded to produce lossless and lossy images, specific spatial regions of the image or images with different quality and resolution. The syntax contains pointers and length fields which allow relevant coded data to be identified without the entropy coder or transforms. Consequently, the codestream can be 'parsed' to create different codestreams without transcoding. For example, such parsing operation can create a new codestream which contains a lower resolution image (e.g. thumbnail) of the original codestream. If a codestream is produced appropriately, it can also be converted to codestreams of lower quality images, or images containing only a specific spatial region. This feature is very useful for many applications, especially on the internet.
While a losslessly compressed facsimile image might require 20,000 bytes of storage, a losslessly compressed color high resolution scan of the same sized document might require 200,000,000 bytes of storage. This factor of 10,000 in the image size necessitates more than just better compression, it requires a change in viewpoint about compression. A compression system for high quality images must provide a way to access only the required data rather than decompressing all the data and then selecting the desired portion. Furthermore, a high quality image compression system should be able to provide the best possible images for output devices which as of yet have not been manufactured. Finally, a high quality compression system should allow decompression and recompression without continual degradation of the image. This paper describes technologies including a reversible color transform, a reversible wavelet transform, a doubly embedded context mode, and a 'parseable' file format, which work together to provide solutions for high quality imaging needs.
We consider the computational complexity of block transform coding and tradeoffs among computation, bit rate, and distortion. In particular, we illustrate a method of coding that allows decompression time to be traded with bit rate under a fixed quality criteria, or allows quality to be traded for speed with a fixed average bit rate. We provide a brief analysis of the entropy coded infinite uniform quantizer that leads to a simple bit allocation for transform coefficients. Finally, we consider the computational cost of transform coding for both the discrete cosine transform (DCT) and the Karhunen-Loeve transform (KLT). In general, a computation-rate- distortion surface can be used to select the appropriate size transform and the quantization matrix for a given bandwidth/CPU channel.
We show how several basic image compression methods (predictive coding, transform coding, and pyramid coding) are based on self-similarity, and a 1/f2 power law. Phase transitions often show self-similarity which is characterized by a spectral power law. Natural images often show a self-similarity which is also characterized by a power law spectrum which is near 1/f2. Exploring physical analogs leads to greater unity among current methods of compression and perhaps lead to improved techniques.