Lofi Rosa Menkman
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Exploring the .BMP File Format
Exploring the .BMP File Format Don Lancaster Synergetics, Box 809, Thatcher, AZ 85552 copyright c2003 as GuruGram #14 http://www.tinaja.com [email protected] (928) 428-4073 The .BMP image standard is used by Windows and elsewhere to represent graphics images in any of several different display and compression options. The .BMP advantages are that each pixel is usually independently available for any alteration or modification. And that repeated use does not normally degrade the image. Because lossy compression is not used. Its main disadvantage is that file sizes are usually horrendous compared to JPEG, fractal, GIF, or other lossy compression schemes. A comparison of popular image standards can be found here. I’ve long been using the .BMP format for my eBay and my other phototography, scanning, and post processing. I firmly believe that… All photography, scanning, and all image post-processing should always be done using .BMP or a similar non-lossy format. Only after all post-processing is complete should JPEG or another compressed distribution format be chosen. Some current examples of my .BMP work now do include the IMAGIMAG.PDF post-processing tutorial, the Bitmap Typewriterthat generates fully anti-aliased small fonts, the Aerial Photo Combiner, and similar utilities and tutorials found on our Fonts & Images, PostScript, and on our Acrobat library pages. A few projects of current interest involving .BMP files include true view camera swings and tilts for a digital camera, distortion correction, dodging & burning, preventing white punchthrough on knockouts, and emphasis vignetting. Mainly applied to uncompressed RGBX 24-bit color .BMP files. -
Music 422 Project Report
Music 422 Project Report Jatin Chowdhury, Arda Sahiner, and Abhipray Sahoo October 10, 2020 1 Introduction We present an audio coder that seeks to improve upon traditional coding methods by implementing block switching, spectral band replication, and gain-shape quantization. Block switching improves on coding of transient sounds. Spectral band replication exploits low sensitivity of human hearing towards high frequencies. It fills in any missing coded high frequency content with information from the low frequencies. We chose to do gain-shape quantization in order to explicitly code the energy of each band, preserving the perceptually important spectral envelope more accurately. 2 Implementation 2.1 Block Switching Block switching, as introduced by Edler [1], allows the audio coder to adaptively change the size of the block being coded. Modern audio coders typically encode frequency lines obtained using the Modified Discrete Cosine Transform (MDCT). Using longer blocks for the MDCT allows forbet- ter spectral resolution, while shorter blocks have better time resolution, due to the time-frequency trade-off known as the Fourier uncertainty principle [2]. Due to their poor time resolution, using long MDCT blocks for an audio coder can result in “pre-echo” when the coder encodes transient sounds. Block switching allows the coder to use long MDCT blocks for normal signals being encoded and use shorter blocks for transient parts of the signal. In our coder, we implement the block switching algorithm introduced in [1]. This algorithm consists of four types of block windows: long, short, start, and stop. Long and short block windows are simple sine windows, as defined in [3]: (( ) ) 1 π w[n] = sin n + (1) 2 N where N is the length of the block. -
Free Lossless Image Format
FREE LOSSLESS IMAGE FORMAT Jon Sneyers and Pieter Wuille [email protected] [email protected] Cloudinary Blockstream ICIP 2016, September 26th DON’T WE HAVE ENOUGH IMAGE FORMATS ALREADY? • JPEG, PNG, GIF, WebP, JPEG 2000, JPEG XR, JPEG-LS, JBIG(2), APNG, MNG, BPG, TIFF, BMP, TGA, PCX, PBM/PGM/PPM, PAM, … • Obligatory XKCD comic: YES, BUT… • There are many kinds of images: photographs, medical images, diagrams, plots, maps, line art, paintings, comics, logos, game graphics, textures, rendered scenes, scanned documents, screenshots, … EVERYTHING SUCKS AT SOMETHING • None of the existing formats works well on all kinds of images. • JPEG / JP2 / JXR is great for photographs, but… • PNG / GIF is great for line art, but… • WebP: basically two totally different formats • Lossy WebP: somewhat better than (moz)JPEG • Lossless WebP: somewhat better than PNG • They are both .webp, but you still have to pick the format GOAL: ONE FORMAT THAT COMPRESSES ALL IMAGES WELL EXPERIMENTAL RESULTS Corpus Lossless formats JPEG* (bit depth) FLIF FLIF* WebP BPG PNG PNG* JP2* JXR JLS 100% 90% interlaced PNGs, we used OptiPNG [21]. For BPG we used [4] 8 1.002 1.000 1.234 1.318 1.480 2.108 1.253 1.676 1.242 1.054 0.302 the options -m 9 -e jctvc; for WebP we used -m 6 -q [4] 16 1.017 1.000 / / 1.414 1.502 1.012 2.011 1.111 / / 100. For the other formats we used default lossless options. [5] 8 1.032 1.000 1.099 1.163 1.429 1.664 1.097 1.248 1.500 1.017 0.302� [6] 8 1.003 1.000 1.040 1.081 1.282 1.441 1.074 1.168 1.225 0.980 0.263 Figure 4 shows the results; see [22] for more details. -
Automatic Detection of Perceived Ringing Regions in Compressed Images
International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 4, Number 5 (2011), pp. 491-516 © International Research Publication House http://www.irphouse.com Automatic Detection of Perceived Ringing Regions in Compressed Images D. Minola Davids Research Scholar, Singhania University, Rajasthan, India Abstract An efficient approach toward a no-reference ringing metric intrinsically exists of two steps: first detecting regions in an image where ringing might occur, and second quantifying the ringing annoyance in these regions. This paper presents a novel approach toward the first step: the automatic detection of regions visually impaired by ringing artifacts in compressed images. It is a no- reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). To maintain low complexity for real-time applications, the proposed approach adopts a perceptually relevant edge detector to capture regions in the image susceptible to ringing, and a simple yet efficient model of visual masking to determine ringing visibility. Index Terms: Luminance masking, per-ceptual edge, ringing artifact, texture masking. Introduction In current visual communication systems, the most essential task is to fit a large amount of visual information into the narrow bandwidth of transmission channels or into a limited storage space, while maintaining the best possible perceived quality for the viewer. A variety of compression algorithms, for example, such as JPEG[1] and MPEG/H.26xhave been widely adopted in image and video coding trying to achieve high compression efficiency at high quality. These lossy compression techniques, however, inevitably result in various coding artifacts, which by now are known and classified as blockiness, ringing, blur, etc. -
Implementation of LSB Steganography and Its Evaluation for Various File Formats (LSB, JSTEG)
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 6, June - 2013 Implementation of LSB Steganography and its Evaluation for Various File Formats (LSB, JSTEG) Vivek Kumar, Sandesh Kumar, Lavalee Singh, Prateek Yadav MANGALAYATAN UNIVERSITY1, 2,3,4 ALIGARH Keywords: Steganography, Least ABSTRACT: Significant Bit (LSB), GIF, PNG, BMP. Steganography is derived from the Greek word steganos which literally means 1. INTRODUCTION “Covered” and graphy means “Writing”, In the current trends of the world, the i.e. covered writing. Steganography refers technologies have advanced so much that to the science of “invisible” Most of the individuals prefer using the communication. For hiding secret internet as the primary medium to transfer information in various file formats, there data from one end to another across the exists a large variety of steganographic IJERTIJERTworld. There are many possible ways to techniques some are more complex than transmit data using the internet: via e-mails, others and all of them have respective chats, etc. The data transition is made very strong and weak points. The Least simple, fast and accurate using the internet. Significant Bit (LSB) embedding However, one of the main problems with technique suggests that data can be sending data over the internet is the security hidden in the least significant bits of the threat it poses i.e. the personal or cover image and the human eye would be confidential data can be stolen or hacked in unable to notice the hidden image in the many ways. Therefore it becomes very cover file. This technique can be used for important to take data security into hiding images in 24-Bit, 8- Bit, Gray scale consideration, as it is one of the most format. -
'" )Timizing Images ) ~I
k ' Anyone wlio linn IIVIII IIMd tliu Wub hns li kely 11H I11tl liy ulow lontlin g Wob sites. It's ', been fn1 11 the ft 1111 cl nlli{lll n1 mlllllll wh111 o th o fil o si of t/11 1 f /11tpli1 LII lt ill llliltl lltt 111t n huw fnst Sllll/ 111 111 11 I 1111 VIIIW 11 111 111 , Mllki l1(1 111't1 11 11 7 W11 1! 1/lllpliiL" 111 llll lllillnti lltlllllltll>ll. 1 l iitllllllllnl 111111 '" )timizing Images y, / l1111 11 11 11111' Lil'J C:ii') I1111 Vi llll ihllillnn l 111 1111 / 11 / ~il/ 11/ y 11111111 In l1 nlp Yll ll IIIIIMIIII IIII I/ 1 11111 I Optimizing JPEGs I 1 I Selective JPEG Optimization with Alpha Channels I l 11 tp111 11 1111 II 1111 111 I 1111 !11 11 1 IIIII nlllll I Optimizing GIFs I Choosing the Right Color Reduction Palette I lljllillll/11 111111 Ill II lillllljliiiX 111 111/r ll I lilnl illllli l 'lilltlllllllip (;~,:; 111 111 I Reducing Colors I Locking Colors I Selective G/F Optimization I illtnrlln lll ll" lllt l" "'ll" i ~IIIH i y C}l •; I Previewing Images in a Web Browser I llnt11tl. II 111 111/ M ullt.li 11 n tlttlmt, 1111111' I Optimizing G/Fs and JPEGs in lmageReady CS2 I IlVII filllll/11111, /111 t/11/l lh, }/ '/ CJ, lind ) If unlnmdtltl In yn tl, /hoy won't chap_ OJ 11111 bo lo1 ""'II· II yo 11'1 11 n pw nt oplimi1 PCS2Web HOT CD-ROM I ing Wol1 IJIIipluc;u, you'll bo impronml d by th o llllpOI'b optimiw ti on cupnbilitlos in ,,, Photoshop CS7 und lmng oRondy CS?. -
(A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format. -
Understanding Image Formats and When to Use Them
Understanding Image Formats And When to Use Them Are you familiar with the extensions after your images? There are so many image formats that it’s so easy to get confused! File extensions like .jpeg, .bmp, .gif, and more can be seen after an image’s file name. Most of us disregard it, thinking there is no significance regarding these image formats. These are all different and not cross‐ compatible. These image formats have their own pros and cons. They were created for specific, yet different purposes. What’s the difference, and when is each format appropriate to use? Every graphic you see online is an image file. Most everything you see printed on paper, plastic or a t‐shirt came from an image file. These files come in a variety of formats, and each is optimized for a specific use. Using the right type for the right job means your design will come out picture perfect and just how you intended. The wrong format could mean a bad print or a poor web image, a giant download or a missing graphic in an email Most image files fit into one of two general categories—raster files and vector files—and each category has its own specific uses. This breakdown isn’t perfect. For example, certain formats can actually contain elements of both types. But this is a good place to start when thinking about which format to use for your projects. Raster Images Raster images are made up of a set grid of dots called pixels where each pixel is assigned a color. -
Dithering and Quantization of Audio and Image
Dithering and Quantization of audio and image Maciej Lipiński - Ext 06135 1. Introduction This project is going to focus on issue of dithering. The main aim of assignment was to develop a program to quantize images and audio signals, which should add noise and to measure mean square errors, comparing the quality of the quantized images with and without noise. The program realizes fallowing: - quantize an image or audio signal using n levels (defined by the user); - measure the MSE (Mean Square Error) between the original and the quantized signals; - add uniform noise in [-d/2,d/2], where d is the quantization step size, using n levels; - quantify the signal (image or audio) after adding the noise, using n levels (user defined); - measure the MSE by comparing the noise-quantized signal with the original; - compare results. The program shows graphic result, presenting original image/audio, quantized image/audio and quantized with dither image/audio. It calculates and displays the values of MSE – mean square error. 2. DITHERING Dither is a form of noise, “erroneous” signal or data which is intentionally added to sample data for the purpose of minimizing quantization error. It is utilized in many different fields where digital processing is used, such as digital audio and images. The quantization and re-quantization of digital data yields error. If that error is repeating and correlated to the signal, the error that results is repeating. In some fields, especially where the receptor is sensitive to such artifacts, cyclical errors yield undesirable artifacts. In these fields dither is helpful to result in less determinable distortions. -
Glitch Studies Manifesto
[email protected]. Amsterdam/Cologne, 2009/2010 http://rosa-menkman.blogspot.com The dominant, continuing search for a noiseless channel has been, and will always be no more than a regrettable, ill-fated dogma. Even though the constant search for complete transparency brings newer, ‘better’ media, every one of these new and improved techniques will always have their own fingerprints of imperfection. While most people experience these fingerprints as negative (and sometimes even as accidents) I emphasize the positive consequences of these imperfections by showing the new opportunities they facilitate. In the beginning there was only noise. Then the artist moved from the grain of celluloid to the magnetic distortion and scanning lines of the cathode ray tube. he wandered the planes of phosphor burn-in, rubbed away dead pixels and now makes performance art based on the cracking of LCD screens. The elitist discourse of the upgrade is a dogma widely pursued by the naive victims of a persistent upgrade culture. The consumer only has to dial #1-800 to stay on top of the technological curve, the waves of both euphoria and disappointment. It has become normal that in the future the consumer will pay less for a device that can do more. The user has to realize that improving is nothing more than a proprietary protocol, a deluded consumer myth about progression towards a holy grail of perfection. Dispute the operating templates of creative practice by fighting genres and expectations! I feel stuck in the membranes of knowledge, governed by social conventions and acceptances. As an artist I strive to reposition these membranes; I do not feel locked into one medium or between contradictions like real vs. -
Determining the Need for Dither When Re-Quantizing a 1-D Signal Carlos Fabián Benítez-Quiroz and Shawn D
µ ˆ q Determining the Need for Dither when Re-Quantizing a 1-D Signal Carlos Fabián Benítez-Quiroz and Shawn D. Hunt Electrical and Computer Engineering Department University of Puerto Rico, Mayagüez Campus ABSTRACT Test In The Frequency Domain Another test signal was used real audio with 24 bit precision and a 44.1 kHz sampling rate. The tests were This poster presents novel methods for determining if dither applied and the percentage of accepted frames is shown in The test in frequency is based on the Fourier transform of the sample figure 3 is needed when reducing the bit depth of a one dimensional autocorrelation. The statistic used is the square of the norm of the DFT digital signal. These are statistical based methods in both the multiplied by scale factor. This is also summarized in Table 1. time and frequency domains, and are based on determining Experiments were also run after adding dither. Because of whether the quantization noise with no dither added is white. Table 1. Summary of the statistics used in the tests. the added dither, all tests should reveal that no additional If this is the case, then no undesired harmonics are added in dither is needed. For the tests, segments from the previous the quantization or re-quantization process. Experiments Test Domain Estimator used Distribution Summary of experiment that are known to need dither are dithered and showing the effectiveness of the methods with both synthetic of Estimator Method these used as the test signal. and real one dimensional signals are presented. Box-Pierce Time Say the quantization Test m error is white when Introduction = 2 χ2 Q N r Q ~ m Qbp is greater than a bp k =1 k bp Digital signals have become widespread, and are favored in set threshold. -
The Theory of Dithered Quantization
The Theory of Dithered Quantization by Robert Alexander Wannamaker A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Applied Mathematics Waterloo, Ontario, Canada, 2003 c Robert Alexander Wannamaker 2003 I hereby declare that I am the sole author of this thesis. I authorize the University of Waterloo to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize the University of Waterloo to reproduce this thesis by pho- tocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. ii The University of Waterloo requires the signatures of all persons using or pho- tocopying this thesis. Please sign below, and give address and date. iii Abstract A detailed mathematical model is presented for the analysis of multibit quantizing systems. Dithering is examined as a means for eliminating signal-dependent quanti- zation errors, and subtractive and non-subtractive dithered systems are thoroughly explored within the established theoretical framework. Of primary interest are the statistical interdependences of signals in dithered systems and the spectral proper- ties of the total error produced by such systems. Regarding dithered systems, many topics of practical interest are explored. These include the use of spectrally shaped dithers, dither in noise-shaping systems, the efficient generation of multi-channel dithers, and the uses of discrete-valued dither signals. iv Acknowledgements I would like to thank my supervisor, Dr. Stanley P. Lipshitz, for giving me the opportunity to pursue this research and for the valuable guidance which he has provided throughout my time with the Audio Research Group.