Source: https://patents.google.com/patent/US6223162B1/en
Timestamp: 2018-04-23 00:16:43
Document Index: 513255347

Matched Legal Cases: ['art 3', 'art 3', 'art 7', 'art 7', 'art 7', 'art 7']

US6223162B1 - Multi-level run length coding for frequency-domain audio coding - Google Patents
Multi-level run length coding for frequency-domain audio coding Download PDF
US6223162B1
US6223162B1 US09211277 US21127798A US6223162B1 US 6223162 B1 US6223162 B1 US 6223162B1 US 09211277 US09211277 US 09211277 US 21127798 A US21127798 A US 21127798A US 6223162 B1 US6223162 B1 US 6223162B1
US09211277
A technique for entropy coding information relating to frequency domain audio coefficients. For portions of a frequency spectrum having a predominate value of zero, a multi-level run length encoder statistically correlates sequences of zero values with non-zero values and assigns variable length code words. An encoder uses a specialized code book generated with respect to the probability of receiving an input sequence of zero-valued spectral coefficients followed by a non-zero coefficient. A corresponding decoder associates a variable length code word with a sequence of zero value coefficients adjacent a non-zero value coefficient.
The present invention relates to setting one or more thresholds to divide an input signal into multiple regions, and applying entropy encoding to encode a variable length first region with a variable length entropy code, and applying a second coding method to a second region.
In a typical audio coding environment, data is represented as a long sequence of symbols which is input to an encoder. The input data is encoded by an encoder, transmitted over a communication channel (or simply stored), and decoded by a decoder. During encoding, the input is pre-processed, sampled, converted, compressed or otherwise manipulated into a form for transmission or storage. After transmission or storage, the decoder attempts to reconstruct the original input.
Audio coding techniques can be generally categorized into two classes, namely the time-domain techniques and frequency-domain ones. Time-domain techniques, e.g., ADPCM, LPC, operate directly in the time domain while the frequency-domain techniques transform the audio signals into the frequency domain where compression is performed. Frequency-domain codecs (compressors/decompressors) can be further separated into either subband or transform coders, although the distinction between the two is not always clear. That is, sub-band coders typically use bandpass filters to divide an input signal into a small number (e.g., four) of sub-bands, whereas transform coders typically have many sub-bands (and therefore a correspondingly large number of transform coefficients). Processing an audio signal in the frequency domain is motivated by both classical signal processing theories and human perception psychoaoustics model.
Psychoacoustics take advantage of known properties of the listener in order to reduce information content. For example, the inner ear, specifically the basilar membrane, behaves like a spectral analyzer and transforms the audio signal into spectral data before further neural processing proceeds. Frequency-domain audio codecs often take advantage of auditory masking that is occurring in the human hearing system by modifying an original signal to eliminate information redundancies. Since human ears are incapable of perceiving these modifications, one can achieve efficient compression without distortion.
Masking analysis is usually conducted in conjunction with quantization so that quantization noise can be conveniently “masked.” In modern audio coding techniques, the quantized spectral data are usually further compressed by applying entropy coding, e.g., Huffman coding. Compression is required because communication channels usually have limited available capacity or bandwidth. It is frequently necessary to reduce the information content of input data in order to allow it to be reliably transmitted, if at all, over the communication channel.
Tremendous effort has been invested in developing lossless and lossy compression techniques for reducing the size of data to transmit or store. One popular lossless technique is Huffman encoding, which is a particular form of entropy encoding. Entropy coding assigns code words to different input sequences, and stores all input sequences in a code book. The complexity of entropy encoding depends on the number m of possible values an input sequence X may take. For small m, there are few possible input combinations, and therefore the code book for the messages can be very small (e.g., only a few bits are needed to unambiguously represent all possible input sequences). For digital applications, the code alphabet is most likely a series of binary digits {0, 1}, and code word lengths are measured in bits.
By their nature, however, most compression techniques for audiovisual data are lossy processes. The level of quality and fidelity delivered in sound and video files depends primarily on how much bandwidth is available and whether the compressor/de-compressor (codec) is optimized to prepare output for an available bandwidth.
The invention relates to a method for entropy coding information relating to frequency domain audio coefficients. In particular, the invention relates to a form of multi-level encoding audio spectral frequency coefficients. Illustrated embodiments are particularly adapted to coding environments in which multiple encoding methods can be chosen based on statistical profiles for frequency coefficient ranges. Encoding methods can be optimized for portions of a frequency spectrum, such as portions having a predominate value.
In illustrated embodiments, the predominate value in certain frequency ranges are zero value frequency coefficients, and they are encoded with the multi-level run-length encoder (RLE) encoder. The multi-level encoder statistically correlates sequences of zero values with one or more non-zero symbols and assigns variable length code words to arbitrarily long input sequences of such zero and non-zero values. The RLE based entropy encoder uses a specialized code book generated with respect to the probability of receiving an input sequence of zero-valued spectral coefficients followed by a non-zero spectral coefficient. If code book size is an issue, the probabilities can be sorted and less probable input sequences excluded from the code book.
Similarly, the range containing mostly non-zero values is encoded with a variable-to-variable entropy encoder, where a variable length code word is assigned to arbitrarily long input sequences of quantization symbols. An overall more efficient process is achieved by basing coding methods according to the properties of the input data. In practice, the number of partitions and frequency ranges will vary according to the type of data to be encoded and decoded.
FIG. 1 is a block diagram of a computer system that may be used to implement run-length coding for frequency domain audio coding.
FIG. 3 illustrates a frequency range divided according to statistical properties for audio data frequency coefficients.
FIG. 5 illustrates a threshold grid used in encoding audio sequences having repeating spectral coefficients.
FIG. 6 illustrates code book creation in accordance with a FIG. 5 threshold grid.
FIG. 7 is a flowchart for encoding (or decoding) audio data with the FIG. 6 code book.
FIG. 1 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. While the invention will be described in the general context of computer-executable instructions of a computer program that runs on a personal computer, those skilled in the art will recognize that the invention also may be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The illustrated embodiment of the invention also is practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. But, some embodiments of the invention can be practiced on stand alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
With reference to FIG. 1, an exemplary system for implementing the invention includes a computer 20, including a processing unit 21, a system memory 22, and a system bus 23 that couples various system components including the system memory to the processing unit 21. The processing unit may be any of various commercially available processors, including Intel x86, Pentium and compatible microprocessors from Intel and others, the Alpha processor by Digital, and the PowerPC from IBM and Motorola. Dual microprocessors and other multi-processor architectures also an be used as the processing unit 21.
A user may enter commands and information into the computer 20 through a keyboard 40 and pointing device, such as a mouse 42. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 21 through a serial port interface 46 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or a universal serial bus (USB). A monitor 47 or other type of display device is also connected to the system bus 23 via an interface, such as a video adapter 48. In addition to the monitor, personal computers typically include other peripheral output devices (not shown), such as speakers and printers.
As illustrated, source data 200 is input to a time/frequency transform encoder 202 such as a filter bank or discrete-cosine type transform. Transform encoder 202 is designed so as to convert a continuous or sampled time-domain input, such as an audio data source, into multiple frequency bands of predetermined (although perhaps differing) bandwidth. These bands can then be analyzed with respect to a human auditory perception model 204 (for example, a psychoacoustic model) in order to determine components of the signal that may be safely reduced without audible impact. For example, it is well known that certain frequencies are inaudible when certain other sounds or frequencies are present in the input signal (simultaneous masking). Consequently, such inaudible signals can be safely removed from the input signal. Use of human auditory models is well known, e.g., the MPEG 1, 2 and 4 standards. (Note that such models may be combined into a quantization 206 operation.)
After performing the time/frequency transformation 202, frequency coefficients within each range are quantized 206 to convert each coefficient (amplitude levels) to a value taken from a finite set of possible values, where each value has a size based on the bits allocated to representing the frequency range. The quantizer may be a conventional uniform or non-uniform quantizer, such as a midriser or midtreader quantizer with (or without) memory. The general quantization goal is identifying an optimum bit allocation for representing the input signal data, i.e., to distribute usage of available encoding bits to ensure encoding the (acoustically) significant portions of the source data. Various quantization methods, such as quantization step size prediction to meet a desired bit rate (assuming constant bit rate) can be used. After the source 200 has been quantized, the resultant data is then entropy encoded 208.
A particularly effective entropy encoding method for mostly non-zero data one that assigns variable length codes to variable length input (a variable-to-variable encoder), as disclosed in a contemporaneously filed application entitled “Entropy Code Mode Switching for Frequency-domain Audio Coding,” bearing Ser. No. 09/211,531; this application is incorporated herein by reference.
A more optimal method is to adaptively locate a partition by performing an exhaustive search to determine an “optimal” boundary location. That is, an optimal solution is to try every frequency (or subset thereof) as a partition location, perform entropy coding, and track which boundary potential position yielded a minimum number of bits for encoding. Although computationally more intensive, if computation costs are at issue, the compression benefits of an exhaustive search (or near exhaustive if frequency subsets are used) can outweigh costs when multiple partitions are used.
In the illustrated embodiment, the selected dividing criteria for the range 300 is the probability C(F) (Y-axis) that a particular spectral event is a run of coefficients at or near a particular intensity (e.g., zero). As with code book generation, the probability of receiving zero value data can be pre-computed with respect to exemplary input. As illustrated, the input signal 308 has high probability of being zero after the indicated partition 306. The position of the partition divider 306 is chosen so that a certain amount of the input beyond the divider, such as 80%, is at or near (and thus equated to) a predetermined value. (In illustrated embodiments, the predetermined value is zero.)
For encoding the mostly non-zero range 310, the variable-to-variable encoder incorporated herein above may be used. For the mostly zero-value range 312, an encoder optimized for such data is used. In the illustrated embodiment, a run length encoder (RLE) is used as it is optimized for encoding data that has a predominate value (e.g., zero). FIG. 5 illustrates one RLE-based entropy encoder that can efficiently encode the mostly zero valued range 312.
FIG. 4 illustrates a transmission model for transmitting audio data over a channel (see FIG. 2), in which multiple entropy encoding/decoding methods are used to manipulate input data 200. It is known that the source audio data 200 will have values within some frequency range. As discussed above for FIG. 2, source data 200 may be converted 202 into the frequency domain, reduced according to perception models 204, and quantized 206. Since quantization may produce significant numbers of near zero output values, an entropy encoder 208 can be optimized to encode this quantization output.
After quantization, the spectral coefficients for the quantized data tend to track the information content of typical audio data. Analysis of the quantization coefficients shows they are most likely non-zero at lower frequency ranges, and mostly zero coefficients at higher frequencies.
Therefore, for frequency partitions located at certain frequency positions, a mode selector 400 can determines which encoder to according to the frequency range being encoded.
As shown, there are N pre-defined encoders 402-406, each optimized to encode a frequency range having data with some predominate characteristic. This does not mean that there are necessarily N distinct input ranges, as different frequency ranges may have similar statistical characteristics for its data, and hence use the same encoder. In the illustrated example, there are only two ranges (one partition), corresponding to low (mostly non-zero coefficients) and high (mostly zero coefficients) frequency ranges. Hence, the mostly zero data past the partition is encoded with an RLE type encoder (see, e.g., FIG. 5), and the data before the partition is encoded with a variable-to-variable entropy-type entropy encoder.
After determining which encoder 402-406 to use, processing continues as discussed with respect FIG. 2 for transmitting data to a receiver 216 for decoding. Note that an inverse mode selector is not shown. A decoder selector is necessary (e.g., as part of the FIG. 2 decoder 212) to properly select an appropriate decoder to reverse the work of the mode selector 400. However, as discussed above, range divider locations can be determined in advance, thus leaving their identification implied during decoding. Or, for dynamic adaptive encoding/decoding, embedded flags may be used to trigger decoder selection. Using flags is equivalent to using a mode selector, and the mode selector can be designed to operate for both pre-determined and adaptively located partitions.
FIG. 5 illustrates a threshold grid that can be used to modify creation of an entropy code book. The upper left corner represents highest values, and the lower right corner represents lowest values. As discussed for FIGS. 3 and 4, encoding becomes more efficient when encoders can be tailored to process certain portions of the input data. In particular, when it is known that the encoding method will produce a significant number of repeating frequency coefficients, an entropy coder can be combined with RLE-type encoding to increase coding efficiency for data containing the repeated value.
In the illustrated embodiments, quantization of the input data introduces zero or near-zero spectral coefficients for significant portions of the frequency ranges for the input data. Consequently, rather than applying the same entropy coder used for the mostly non-zero data (e.g., the encoder and code book of FIG. 5), instead a RLE-based entropy coder is used. To do so, a special entropy code book is constructed (see FIG. 6). Although it is assumed that RLE compressible data is mostly runs of zero spectral coefficients, note that the disclosed methods are applicable to any repeating data values.
The left edge Rj 502 of the grid 500 represents zero value runs Rj in the input stream having length at or exceeding j, where j ranges from a length of a zero length run to an m-length run. The value of m is a known maximum length zero-value run based on analysis of exemplary input, or from dynamic inspection of incoming data. The top edge of the grid represents spectral coefficient values Li having intensity at or exceeding i, where i ranges from a minimum intensity of zero, up to a maximum intensity n. As with determining the maximum value m, n is determined according to exemplary input or currently received input.
The grid pictorially presents computation of the probability of receiving a run of zero (or other predetermined) value coefficients R having length of at least j that is followed by a spectral intensity value of i in intensity. The darker squares, e.g., 504, 506, correspond to event pairings (Rj,Li) having higher probability, and are collectively referenced as region 508. The lighter squares, e.g., 510, 512, 514, represent pairings having low probability, such as long zero runs followed by high spectral coefficients, and are collectively referenced as region 516. The graph shows that short zero-value runs followed by low spectral values are the most likely input sequences to be received, and that as run lengths get longer, or spectral values get higher, the probability of such combination is reduced.
During entropy code book construction, the improbable pairings are excluded from the code book. Exclusion is made with respect to a selected probability threshold 518. Event pairings below the threshold are deemed insufficiently probable and are excluded from the code book. The result of the exclusion is to limit code book size, while also allowing very efficient encoding of probable input sequences having sequences of zero values.
FIG. 6 illustrates constructing an entropy code book in accordance with a FIG. 5 spectral threshold grid. As a more formal description of the contents of FIG. 5, let the absolute values of non-zero spectral samples form an integer set Li={1, 2, 3, . . . , Ln} where Ln stands for any value that is greater than or equal to Ln (corresponding to the FIG. 5 top grid edge). Let the run length of zero spectral samples in an input stream form another set Rj={1, 2, 3, . . . , Rm} where Rm stands for any zero runs with length equal to or longer than Rm (corresponding to the FIG. 5 left grid edge). Using this notation, we can represent an input of frequency coefficients with a string of input symbols defined as (Rj, Li), which corresponds to Rj zero spectral samples followed by a non-zero spectral sample having intensity of at least Li.
This method defines entropy codes with respect to the probability of receiving certain zero run lengths along with non-zero input contrasts prior art methods, and therefore performs an RLE-type of encoding as input sequences assigned codes include runs of repeating symbols (e.g., the zero value symbols). To create a code book for the illustrated embodiment, a preliminary step is to collect the probability of occurrence 600 for each possible pairing. The computed probabilities can then be sorted 602, and a probability threshold 518 (FIG. 5) applied 604. Application of the threshold operates to exclude all input pairings in FIG. 5 grid region 516. Entropy codes are then assigned 606 to remaining pairings in FIG. 5 region 508.
There are various techniques available for storing and manipulating the code book. For example, one structure is storing the two-dimensional grid in permanent storage, where data retrieval is performed by identifying two indices. Thus, one can retrieve table entries by specification of a run-length and a particular symbol value. A decoding table can be implemented as a Huffman tree. Another code book implementation includes Rice-Golomb structures, and their equivalents.
FIG. 7 shows one method for encoding data with RLE-based entropy encoder of FIGS. 5 and 6. It is assumed that encoding has proceeded as illustrated in FIG. 4, until mode selector 400 determines that RLE encoding is required. As illustrated, a series of spectral coefficients are received 650. This input is scanned 652 to identify an input sequence having zero value spectral samples followed by a non-zero spectral sample. This sequence is then looked up 654 in the code book of FIG. 6, and the corresponding code is output 656.
If the particular scanned sequence 652 is not found within the code book, then it is known to have originally belonged to the low probability region 516 of FIG. 5. A special output symbol can be inserted into the output data stream to identify this data configuration, along with sufficient information to identify the received event (e.g., a code to identify the number of zero valued samples, followed by the non-zero sample). In the case of an input spectrum ending with N zeros, either a special ending signal is needed or a special event such as (N, 1) suffices because the decoder is aware the total number of samples and able to stop decoding when that limit is exceeded.
Although not explicitly illustrated, decoding operates as an inverse operation of encoding, where the encoded data 656 is looked up 654 in a corresponding FIG. 5 decoding code book, in order to recover the input sequence 652.
1. A method of encoding audio data frequency coefficients, the method comprising:
receiving a sequence of audio data frequency coefficient symbols, wherein the sequence includes a run and an adjacent symbol, the run including R symbols each having a first value, and the adjacent symbol having a value L other than the first value;
looking up the sequence within a data structure that assigns a corresponding entropy code to the sequence based jointly upon the values for R and L for the sequence; and
outputting the corresponding entropy code for the sequence.
2. A method according to claim 1, wherein the first value is zero, and L is non-zero.
3. A computer readable medium having encoded thereon instructions for directing a computer to perform the method of claim 1.
4. The method of claim 1 wherein the corresponding entropy code for the sequence is an escape code.
5. A method of decoding entropy encoded audio data frequency coefficients, comprising:
receiving an entropy code representing a sequence of audio data frequency coefficient symbols;
looking up the entropy code within a data structure that assigns a corresponding sequence of symbols to the entropy code, wherein the corresponding sequence includes a run of symbols having a first value, the run repeating for R symbols, and wherein the corresponding sequence further includes an adjacent symbol having a value L other than the first value; and
outputting the sequence of audio data frequency coefficient symbols corresponding to the entropy code.
determining whether the entropy code is an escape code; and
when the entropy code is an escape code, identifying an escape code event sequence of audio data frequency coefficient symbols, and outputting such escape code event sequence.
7. A computer readable medium having encoded thereon instructions for directing a computer to perform the method of claim 6.
8. A method of creating an entropy code book for encoding audio data frequency coefficient symbols with an entropy encoder, the method comprising:
creating a data structure containing a plurality of pairings for input sequences of audio data frequency coefficient symbols, where each pairing jointly represents a repeating run of symbols having a first value and an adjacent symbol having a value L other than the first value, where the length of the repeating run is R;
determining a probability of occurrence of each pairing; and
assigning an entropy code to each pairing according to its probability of occurrence.
setting a probability threshold;
wherein each pairing having a probability of occurrence below the probability threshold is assigned an escape code.
10. A method according to claim 8, wherein R represents a length value for a run of zero value audio frequency coefficient symbols.
11. A computer readable medium having encoded thereon instructions for directing a computer to perform the method of claim 8.
12. A system for encoding audio data frequency coefficients, the system comprising:
means for receiving a sequence of audio data frequency coefficient symbols, wherein the sequence includes a run and an adjacent non-zero coefficient symbol, the run repeating for R zero coefficient symbols;
means for looking up the sequence within a data structure that assigns a corresponding entropy code to the sequence based jointly upon the values for R and L for the sequence;
means for outputting the corresponding entropy code for the sequence.
a signal transformer for converting a time-domain input signal to a frequency-domain audio sequence; and
a quantizer for quantizing the frequency-domain audio sequence into the sequence of audio data frequency coefficient symbols.
14. A system for decoding entropy encoded audio data frequency coefficients, comprising:
means for receiving an entropy code representing a sequence of audio data frequency coefficient symbols;
means for looking up the entropy code within a data structure that assigns a corresponding sequence of coefficient symbols to the entropy code, wherein the corresponding sequence includes a run of zero coefficient symbols and an adjacent non-zero coefficient, the run repeating for R symbols; and
means for outputting the corresponding sequence of audio data frequency coefficient symbols.
15. A system according to claim 14, in which the entropy encoded audio data frequency coefficients include entropy codes and escape codes followed by audio data frequency coefficient symbols adjacent each such escape code, the method comprising:
means for determining whether the received entropy code is an escape code;
means for identifying audio data frequency coefficient symbols adjacent such escape code; and
means for outputting such symbols adjacent the escape code.
16. A computer-readable medium storing instructions for performing a method of encoding frequency coefficients for audio data, the method comprising:
receiving a set of frequency coefficients for audio data, the set including one or more sequences of coefficients, wherein for each sequence a pairing jointly represents a run length for the sequence and a non-zero value coefficient within the sequence, the run length indicating a run of zero value coefficients within the sequence;
for each of the one or more sequences,
assigning an entropy code based upon the pairing; and
outputting the entropy code.
17. The computer-readable medium of claim 16 wherein for an improbable sequence, the entropy code is an escape code, the method further comprising:
after outputting the escape code, outputting coefficients representative of the improbable sequence.
18. The computer-readable medium of claim 16 wherein for each of the one or more sequences, the run length is zero or more.
19. The computer-readable medium of claim 16 wherein for each of the one or more sequences, the non-zero value coefficient follows the run of zero value coefficients within the sequence.
20. A computer-readable medium storing instructions for performing a method of decoding entropy-coded frequency coefficients for audio data, the method comprising:
receiving one or more entropy codes for a set of frequency coefficients for audio data;
for each of the one or more entropy codes, assigning a sequence of coefficients to the entropy code, the sequence including a non-zero value coefficient and a run of zero value coefficients, wherein the run length of the run is zero or more; and
outputting coefficients based upon the assigned sequences.
21. The computer-readable medium of claim 20 wherein for the sequence, the non-zero value coefficient follows the run of zero value coefficients.
US09211277 1998-12-14 1998-12-14 Multi-level run length coding for frequency-domain audio coding Active US6223162B1 (en)
US09211277 US6223162B1 (en) 1998-12-14 1998-12-14 Multi-level run length coding for frequency-domain audio coding
PCT/US1999/029107 WO2000036753A1 (en) 1998-12-14 1999-12-07 Multi-level run length coding for frequency-domain audio coding
US6223162B1 true US6223162B1 (en) 2001-04-24
ID=22786243
US09211277 Active US6223162B1 (en) 1998-12-14 1998-12-14 Multi-level run length coding for frequency-domain audio coding
US (1) US6223162B1 (en)
WO (1) WO2000036753A1 (en)
US6377930B1 (en) * 1998-12-14 2002-04-23 Microsoft Corporation Variable to variable length entropy encoding
US20030142746A1 (en) * 2002-01-30 2003-07-31 Naoya Tanaka Encoding device, decoding device and methods thereof
EP1400954A2 (en) * 2002-09-04 2004-03-24 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes
US7103554B1 (en) * 1999-02-23 2006-09-05 Fraunhofer-Gesellschaft Zue Foerderung Der Angewandten Forschung E.V. Method and device for generating a data flow from variable-length code words and a method and device for reading a data flow from variable-length code words
US20080284623A1 (en) * 2007-05-17 2008-11-20 Seung Kwon Beack Lossless audio coding/decoding apparatus and method
US20110166864A1 (en) * 2001-12-14 2011-07-07 Microsoft Corporation Quantization matrices for digital audio
WO2013026155A1 (en) * 2011-08-19 2013-02-28 Alexander Zhirkov Multi-structural, multi-level information formalization and structuring method, and associated apparatus
US20130195175A1 (en) * 2002-04-01 2013-08-01 Broadcom Corporation System and method for multi-row decoding of video with dependent rows
CN102210105B (en) 2008-11-10 2014-04-23 苹果公司 System and method for compressing a stream of integer-valued data
US20150319441A1 (en) * 2013-01-30 2015-11-05 Intel Corporation Content adaptive entropy coding of partitions data for next generation video
JPS62247626A (en) 1986-04-19 1987-10-28 Fuji Photo Film Co Ltd Coding method
US4744085A (en) 1985-04-13 1988-05-10 Canon Kabushiki Kaisha Data processing device
EP0535571A2 (en) 1991-09-30 1993-04-07 Eastman Kodak Company Modified Huffman encode/decode system with simplified decoding for imaging systems
EP0540350A2 (en) 1991-10-31 1993-05-05 Sony Corporation Method and apparatus for variable length coding
EP0612156A2 (en) 1989-04-17 1994-08-24 Fraunhofer-Gesellschaft Zur Förderung Der Angewandten Forschung E.V. Digital coding method
JPH09232968A (en) 1996-02-23 1997-09-05 Kokusai Denshin Denwa Co Ltd <Kdd> Variable length code generator
EP0830029A2 (en) 1996-09-13 1998-03-18 Robert Bosch Gmbh Method for video signal data reduction
WO1998040969A2 (en) 1997-03-14 1998-09-17 J.Stream, Inc. Text file compression system
"Information Technology-Very Low Bitrate Audio-Visual Coding," Part 3: Audio, ISO/JTC1/SC29, N2203, May 15, 1998, pp. 4-9, including section on Huffman Coding.
"Information Technology—Very Low Bitrate Audio-Visual Coding," Part 3: Audio, ISO/JTC1/SC29, N2203, May 15, 1998, pp. 4-9, including section on Huffman Coding.
Allen Gersho and Robert M. Gray, Vector Quantization and Signal Compression, "Entropy Coding," 1992, Chap. 9, pp. 259-305.
Fogg, "Survey of Software and Hardware VLC Architectures," SPIE vol. 2186, pp. 29-37 (no date of publication).
Gibson et al., Digital Compression of Multimedia, "Advanced Audio Coding," Chapter 11.6.2, pp. 415-416 (1998).
Gibson et al., Digital Compression of Multimedia, "Frequency Domain Coding," Chapter 7, pp. 227-262 (1998).
Gibson et al., Digital Compression of Multimedia, "Frequency Domain Speech and Audio Coding Standards," Chapter 8, pp. 263-290 (1998).
Gibson et al., Digital Compression of Multimedia, "Lossless Source Coding," Chapter 2, pp. 17-62 (1998).
Gibson et al., Digital Compression of Multimedia, "MPEG Audio," Chapter 11.4, pp. 398-402 (1998).
Gibson et al., Digital Compression of Multimedia, "Universal Lossless Source Coding," Chapter 3, pp. 63-112 (1998).
International Organisation for Standardisation Organisation Internationale De Normalisation, ISO/IEC JTC1/SC29/WG11, N2202, Coding of Moving Pictures and Audio, Tokyo, Mar. 1998.
International Organization for Standardisation ISO/IEC JTCI/SC29/WG11,N2459 "Overview of the MPEG-4 Standard," (Oct. 1998).
ISO/IEC 13818-7: Information Technology-Generic coding of moving pictures and associated audio information-Part 7: Advanced Audio Coding, pp. I-iv, 1-147 (1997).
ISO/IEC 13818-7: Information Technology—Generic coding of moving pictures and associated audio information—Part 7: Advanced Audio Coding, pp. I-iv, 1-147 (1997).
ISO/IEC 13818-7: Information Technology-Generic coding of moving pictures and associated audio information-Part 7: Advanced Audio Coding, Technical Corrigendum 1, pp. 1-22 (Dec. 1998).
ISO/IEC 13818-7: Information Technology—Generic coding of moving pictures and associated audio information—Part 7: Advanced Audio Coding, Technical Corrigendum 1, pp. 1-22 (Dec. 1998).
Stanley N. Baron, Mark I. Krivocheev, "Coding the Audio Signal," Digital Image and Audio Communications, 1996, pp. 101-128.
Tewfik et al., "Enhanced Wavelet Based Audio Coder," Proc. ASILOMAR Conference, IEEE (1993).
Video Coding For Low Bitrate Communication, "Line Transmission of Non-Telephone Signals," Telecommunication Standardization Sector of International Telecommunication Union, Dec. 5, 1995, pp.22-23.
US7353447B2 (en) * 2000-09-12 2008-04-01 At&T Corp System and method for representing compressed information
US7725808B1 (en) * 2000-09-12 2010-05-25 At&T Intellectual Property Ii, Lp System and method for representing compressed information
US7076108B2 (en) * 2001-12-11 2006-07-11 Gen Dow Huang Apparatus and method for image/video compression using discrete wavelet transform
US8428943B2 (en) * 2001-12-14 2013-04-23 Microsoft Corporation Quantization matrices for digital audio
US7299175B2 (en) * 2001-12-14 2007-11-20 Microsoft Corporation Normalizing to compensate for block size variation when computing control parameter values for quality and rate control for digital audio
US7246065B2 (en) * 2002-01-30 2007-07-17 Matsushita Electric Industrial Co., Ltd. Band-division encoder utilizing a plurality of encoding units
US9307236B2 (en) * 2002-04-01 2016-04-05 Broadcom Corporation System and method for multi-row decoding of video with dependent rows
EP2006840A1 (en) * 2002-09-04 2008-12-24 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes
EP1734511A3 (en) * 2002-09-04 2007-06-20 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes
EP1400954A3 (en) * 2002-09-04 2005-02-02 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes
EP2282310A1 (en) 2002-09-04 2011-02-09 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes
EP2267698A1 (en) 2002-09-04 2010-12-29 Microsoft Corporation Entropy coding by adapting coding between level and run-length/level modes.
US7605722B2 (en) 2007-05-17 2009-10-20 Electronics And Telecommunications Research Institute Lossless audio coding/decoding apparatus and method
CN104011792A (en) * 2011-08-19 2014-08-27 亚历山大·日尔科夫 Multi-structural, multi-level information formalization and structuring method and associated apparatus
US9501494B2 (en) 2011-08-19 2016-11-22 General Harmonics International, Inc. Multi-structural, multi-level information formalization and structuring method, and associated apparatus
US9686551B2 (en) * 2013-01-30 2017-06-20 Intel Corporation Content adaptive entropy coding of partitions data for next generation video
WO2000036753A1 (en) 2000-06-22 application
Wang et al. 1997 Multiple description image coding for noisy channels by pairing transform coefficients
US7006568B1 (en) 2006-02-28 3D wavelet based video codec with human perceptual model
US6943710B2 (en) 2005-09-13 Method and arrangement for arithmetic encoding and decoding binary states and a corresponding computer program and a corresponding computer-readable storage medium
EP1047047A2 (en) 2000-10-25 Audio signal coding and decoding methods and apparatus and recording media with programs therefor
US20070162236A1 (en) 2007-07-12 Dimensional vector and variable resolution quantization
US20030171919A1 (en) 2003-09-11 Scalable lossless audio coding/decoding apparatus and method
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