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# Tag Info

## Hot answers tagged compression

9 votes

### Compress a signal by storing signal diff instead of actual samples - is there such a thing?

You can also think of delta encoding as linear predictive coding (LPC) where only the prediction residual ($x[n]-\hat{x}[n]$ in @robertbristow-johnson's notation) is stored and the predictor of the ...
• 13.6k
9 votes

### Discrete Cosine Transform

To illustrate Justme's answer: Discrete Cosine Transform (DCT) is a lossy The DCT can't be a lossy algorithm, since there's an inverse operation that restores the original input exactly. data ...
• 31.8k
6 votes

### Compression algorithms specific to complex signals

Complex signals are a special case of multidimensonal signals (where the dimension is two). A lossy approach tackling compression of multidimensional signals is vector quantization. A very good ...
• 4,305
6 votes

### Compressive Sensing vs. Sparse Coding

As you correctly noted compressed sensing, compressive sampling, sparse sampling all mean the same thing. Some authors also call it sparse sensing. The idea behind compressed sensing is that a sparse ...
• 4,134
6 votes

### Compress a signal by storing signal diff instead of actual samples - is there such a thing?

That's used a lot. See for example https://en.wikipedia.org/wiki/Delta_encoding, https://en.wikipedia.org/wiki/Run-length_encoding. "Looking Smooth" typically means "not a lot of high frequency ...
• 46.7k
6 votes
Accepted

### Compress a signal by storing signal diff instead of actual samples - is there such a thing?

Another notion you might wanna look into for lossless compression of a bandlimited signal (it's this bandlimiting that gets you this "smoother ... signal, ...closer ... to the baseline") is Linear ...
5 votes
Accepted

### Compressive Sensing vs. Sparse Coding

A couple of reference works offer an exaplanation: A neurological interpretation described in Scholarpedia Stanford's Unsupervised Feature Learning and Deep Learning tutorial If we look at the ...
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5 votes

### Filtering vs Compression Paradox

+1 on very interesting and insightful experiment. Some thoughts: It's not true that filtered signal has less information. It depends on your input signal, filter type, and cut-off frequency. When ...
• 273
5 votes

### Discrete Cosine Transform

No, because DCT is not a compression algorithm itself. But different lossy compression algorithms do use DCT as part of the process. DCT can be used to transform data such as audio or image data into ...
• 2,321
4 votes

### Data representation of largest DCT coefficients

Your question is very accurate. Storing only the value of the largest (1 % for instance) coefficients, even from a good sparsifying transform (DCT, wavelet, else) is fool's-gold, since you (more ...
• 32.1k
4 votes

### Data representation of largest DCT coefficients

How is bit reduction achieved practically in JPEG? First, the whole graylevel (8-bit per pixel) image is divided into adjacent blocks of 8 x 8 = 64 pixels. Then, each block is independently DCT ...
• 28.3k
4 votes
Accepted

### About speech preprocessing in mobile phones

Yes, the cellular phones use various forms of compression to convert the captured analog audio (speech) into the digital bitstream for transmission through 2G/3G/.../. The specific method used ...
• 28.3k
4 votes

### Where is truncated-SVD image compression actually used?

A first difficulty with your question is that "usual instances" may not be fully publicized nor documented. A second one is related to the fact that "compression standard" are ...
• 32.1k
3 votes

### compression ratio of pixels block

Compression ratio is $$\frac{N_{uncomp}^b}{N_{comp}^b}$$ where ${N_{comp}^b}$ is the total number bits requried to represent those $3$ pixels $A$,$B$, and $C$ while ${N_{uncomp}^b}$ is the total ...
• 28.3k
3 votes

### Filtering vs Compression Paradox

I would check 2 things: If the filter applied is Low Pass Filter or a different filter. If it is a filter which amplifies the noise, the result is reasonable. It seems that you use ...
• 20.1k
3 votes
Accepted

### A query on the non-uniform quantization

To make it more clear, I suppose your question is Why it is said that the compressor gain at low input amplitudes is higher, while the step size of a nonuniform quantizer is small in that region. ...
• 4,305
3 votes
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### validation of a pseudo jpeg compression

If you compute a $8\times 8$ 2D-DCT, and keep the top left corner only, you are keeping a quantity that is proportional to the average of each $8\times 8$ block. This is the DC component, similar to ...
• 32.1k
3 votes

### References on the MP3 encoding algorithm

A compression standard is a quite delicate thing, that took years to develop and tune. I suggest Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB, 2012 by Jayaraman J. Thiagarajan and ...
• 32.1k
3 votes
Accepted

### How does a Huffman encoded message gets unified before channel encoding?

They don't get unified. Think of the transmitter pipeline (data source, source encoder, channel coder, modulator, etc) as a sequence of independent blocks. Blocks don't assign any particular meaning ...
• 15.4k
3 votes

### Discrete Cosine Transform

Good start. Let us adjust a bit, in an other narrative point of view. Here is the compiled version: Discrete Cosine Transform (DCT) is a lossy data compression algorithm that is used in many ...
• 32.1k
3 votes

### Which Audio Codec/Bitrate Relationship delivers best Audio Quality: 128 kbit/s MP3 vs. 192 kbit/s AAC vs. 64 kbit/s OPUS?

First of: "AAC" is not a single codec; there's AAC-LC, HE-AAC, and probably a lot more. Since "audio quality" is a perceptive quality, there's no objectively "best" ...
• 31.8k
3 votes

### do audio compression algorithms encode channels in parralel?

Also the each track is not actually audio, (and wont oscillate like audio does). Then don't use a lossy audio codec like MP3, AAC, etc. These are PERCEPTUAL codecs, i.e. they are heavily leveraging ...
• 46.7k
3 votes

### What happens when you read and save the same JPEG image again over and over?

JPEG is lossy compression, and it is allowed to do anything deemed beneficial in representing an image as accurately as possible using the minimum amount of storage while keeping cpu load in check. ...
• 3,487
3 votes

### DCT - Measures of energy compaction gain achieved using DCT over FFT

In Discrete-Time Signal Processing by Oppenheim, chapter 8.5, there is a quantification of mean squared error by setting coefficients to 0 for both DCT and DFT: From this, you can of course calculate ...
• 11.1k
3 votes
Accepted

### Will open, edit, store from/to lossy audio format significantly degrade quality?

This really depends a lot on what exactly the processing is. Cutting out unwanted parts of the signal will have very little impact on the parts that you keep. Ogg Vorbis is block based and the largest ...
• 46.7k
2 votes
Accepted

### What is the purpose of dividing a signal into subbands when compressing an audio file?

There are two types of compression algorithms loss-less (like Flac, Apple ALAC, etc.) which are algorithms similar to Huffman coding. If you apply those to raw audio wave files you get a reduction of ...
• 46.7k
2 votes

### What is the purpose of dividing a signal into subbands when compressing an audio file?

We don't hear all frequencies equally well. Therefore, if we split the frequencies into subbands, we can give more bits to bands which we hear well, and less bits to bands we don't hear that well (...
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2 votes

### DCT: Alternatives to quantization

From what I read there are certain quantisation matrices for different applications, Adobe Photoshop has like 15 or something. The idea is that although there are error based mathematical calculations ...
2 votes
Accepted

### Sampling from a distribution with arithmetic coding

While this answer may have come a bit too late for OP, here it is. Let's start by replacing the phrase 'bits used' with 'queries to an unbiased bit generator'. Then assume that the only random number ...
2 votes
Accepted

### Compression algorithms specific to complex signals

Personally, I'd assume that coders that work well on real-valued data would work well on the separated imaginary and real parts too – and after those, another round of trying to compress by exploiting ...
• 31.8k

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