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Weave Your Personal Apollo-Period Reminiscence


The world of magic had Houdini, who pioneered methods which can be nonetheless carried out at present. And information compression has Jacob Ziv.

In 1977, Ziv, working with Abraham Lempel, revealed the equal of
Houdini on Magic: a paper within the IEEE Transactions on Data Concept titled “A Common Algorithm for Sequential Knowledge Compression.” The algorithm described within the paper got here to be referred to as LZ77—from the authors’ names, in alphabetical order, and the yr. LZ77 wasn’t the primary lossless compression algorithm, however it was the primary that would work its magic in a single step.

The next yr, the 2 researchers issued a refinement, LZ78. That algorithm turned the idea for the Unix compress program used within the early ’80s; WinZip and Gzip, born within the early ’90s; and the GIF and TIFF picture codecs. With out these algorithms, we would possible be mailing massive information recordsdata on discs as a substitute of sending them throughout the Web with a click on, shopping for our music on CDs as a substitute of streaming it, and taking a look at Fb feeds that do not have bouncing animated pictures.

Ziv went on to associate with different researchers on different improvements in compression. It’s his full physique of labor, spanning greater than half a century, that earned him the
2021 IEEE Medal of Honor “for elementary contributions to info concept and information compression expertise, and for distinguished analysis management.”

Ziv was born in 1931 to Russian immigrants in Tiberias, a metropolis then in British-ruled Palestine and now a part of Israel. Electrical energy and devices—and little else—fascinated him as a baby. Whereas practising violin, for instance, he got here up with a scheme to show his music stand right into a lamp. He additionally tried to construct a Marconi transmitter from steel player-piano components. When he plugged the contraption in, the complete home went darkish. He by no means did get that transmitter to work.

When the Arab-Israeli Warfare started in 1948, Ziv was in highschool. Drafted into the Israel Protection Forces, he served briefly on the entrance strains till a bunch of moms held organized protests, demanding that the youngest troopers be despatched elsewhere. Ziv’s reassignment took him to the Israeli Air Drive, the place he skilled as a radar technician. When the battle ended, he entered Technion—Israel Institute of Know-how to check electrical engineering.

After finishing his grasp’s diploma in 1955, Ziv returned to the protection world, this time becoming a member of Israel’s Nationwide Protection Analysis Laboratory (now
Rafael Superior Protection Methods) to develop digital elements to be used in missiles and different navy programs. The difficulty was, Ziv remembers, that not one of the engineers within the group, together with himself, had greater than a fundamental understanding of electronics. Their electrical engineering schooling had targeted extra on energy programs.

“We had about six individuals, and we needed to train ourselves,” he says. “We’d choose a e book after which examine collectively, like spiritual Jews learning the Hebrew Bible. It wasn’t sufficient.”

The group’s objective was to construct a telemetry system utilizing transistors as a substitute of vacuum tubes. They wanted not solely data, however components. Ziv contacted Bell Phone Laboratories and requested a free pattern of its transistor; the corporate despatched 100.

“That lined our wants for just a few months,” he says. “I give myself credit score for being the primary one in Israel to do one thing severe with the transistor.”

In 1959, Ziv was chosen as certainly one of a handful of researchers from Israel’s protection lab to check overseas. That program, he says, reworked the evolution of science in Israel. Its organizers did not steer the chosen younger engineers and scientists into explicit fields. As a substitute, they allow them to pursue any sort of graduate research in any Western nation.

“So as to run a pc program on the time, you had to make use of punch playing cards and I hated them. That’s the reason I did not go into actual laptop science.”

Ziv deliberate to proceed working in communications, however he was not fascinated about simply the {hardware}. He had just lately learn
Data Concept (Prentice-Corridor, 1953), one of many earliest books on the topic, by Stanford Goldman, and he determined to make info concept his focus. And the place else would one examine info concept however MIT, the place Claude Shannon, the sector’s pioneer, had began out?

Ziv arrived in Cambridge, Mass., in 1960. His Ph.D. analysis concerned a technique of figuring out easy methods to encode and decode messages despatched by means of a loud channel, minimizing the chance and error whereas on the identical time maintaining the decoding easy.

“Data concept is gorgeous,” he says. “It tells you what’s the greatest you could ever obtain, and [it] tells you easy methods to approximate the end result. So when you make investments the computational effort, you may know you’re approaching the most effective final result potential.”

Ziv contrasts that certainty with the uncertainty of a deep-learning algorithm. It could be clear that the algorithm is working, however no person actually is aware of whether or not it’s the greatest outcome potential.

Whereas at MIT, Ziv held a part-time job at U.S. protection contractor
Melpar, the place he labored on error-correcting software program. He discovered this work much less lovely. “So as to run a pc program on the time, you had to make use of punch playing cards,” he remembers. “And I hated them. That’s the reason I did not go into actual laptop science.”

Again on the Protection Analysis Laboratory after two years in the USA, Ziv took cost of the Communications Division. Then in 1970, with a number of different coworkers, he joined the college of Technion.

There he met Abraham Lempel. The 2 mentioned making an attempt to enhance lossless information compression.

The state-of-the-art in lossless information compression on the time was Huffman coding. This method begins by discovering sequences of bits in an information file after which sorting them by the frequency with which they seem. Then the encoder builds a dictionary wherein the most typical sequences are represented by the smallest variety of bits. This is identical concept behind Morse code: Essentially the most frequent letter within the English language, e, is represented by a single dot, whereas rarer letters have extra advanced mixtures of dots and dashes.

Huffman coding, whereas nonetheless used at present within the MPEG-2 compression format and a lossless type of JPEG, has its drawbacks. It requires two passes by means of an information file: one to calculate the statistical options of the file, and the second to encode the info. And storing the dictionary together with the encoded information provides to the dimensions of the compressed file.

Ziv and Lempel puzzled if they may develop a lossless data-compression algorithm that may work on any form of information, didn’t require preprocessing, and would obtain the most effective compression for that information, a goal outlined by one thing often called the Shannon entropy. It was unclear if their objective was even potential. They determined to seek out out.

Ziv says he and Lempel had been the “good match” to deal with this query. “I knew all about info concept and statistics, and Abraham was effectively geared up in Boolean algebra and laptop science.”

The 2 got here up with the thought of getting the algorithm search for distinctive sequences of bits on the identical time that it is compressing the info, utilizing tips that could seek advice from beforehand seen sequences. This method requires just one go by means of the file, so it is sooner than Huffman coding.

Ziv explains it this manner: “You have a look at incoming bits to seek out the longest stretch of bits for which there’s a match prior to now. For instance that first incoming bit is a 1. Now, since you’ve just one bit, you’ve by no means seen it prior to now, so you don’t have any selection however to transmit it as is.”

“However you then get one other bit,” he continues. “Say that is a 1 as effectively. So that you enter into your dictionary 1-1. Say the following bit is a 0. So in your dictionary you now have 1-1 and likewise 1-0.”

This is the place the pointer is available in. The following time that the stream of bits features a 1-1 or a 1-0, the software program would not transmit these bits. As a substitute it sends a pointer to the placement the place that sequence first appeared, together with the size of the matched sequence. The variety of bits that you simply want for that pointer may be very small.

“Data concept is gorgeous. It tells you what’s the greatest you could ever obtain, and (it) tells you easy methods to approximate the end result.”

“It is principally what they used to do in publishing
TV Information,” Ziv says. “They’d run a synopsis of every program as soon as. If this system appeared greater than as soon as, they did not republish the synopsis. They simply stated, return to web page x.”

Decoding on this manner is even easier, as a result of the decoder would not should determine distinctive sequences. As a substitute it finds the areas of the sequences by following the pointers after which replaces every pointer with a replica of the related sequence.

The algorithm did the whole lot Ziv and Lempel had got down to do—it proved that universally optimum lossless compression with out preprocessing was potential.

“On the time they revealed their work, the truth that the algorithm was crisp and chic and was simply implementable with low computational complexity was nearly inappropriate,” says Tsachy Weissman, {an electrical} engineering professor at Stanford College who focuses on info concept. “It was extra concerning the theoretical outcome.”

Ultimately, although, researchers acknowledged the algorithm’s sensible implications, Weissman says. “The algorithm itself turned actually helpful when our applied sciences began coping with bigger file sizes past 100,000 and even 1,000,000 characters.”

“Their story is a narrative concerning the energy of elementary theoretical analysis,” Weissman provides. “You may set up theoretical outcomes about what needs to be achievable—and many years later humanity advantages from the implementation of algorithms based mostly on these outcomes.”

Ziv and Lempel stored engaged on the expertise, making an attempt to get nearer to entropy for small information recordsdata. That work led to LZ78. Ziv says LZ78 appears just like LZ77 however is definitely very completely different, as a result of it anticipates the following bit. “For instance the primary bit is a 1, so that you enter within the dictionary two codes, 1-1 and 1-0,” he explains. You may think about these two sequences as the primary branches of a tree.”

“When the second bit comes,” Ziv says, “if it is a 1, you ship the pointer to the primary code, the 1-1, and if it is 0, you level to the opposite code, 1-0. And you then lengthen the dictionary by including two extra prospects to the chosen department of the tree. As you do this repeatedly, sequences that seem extra often will develop longer branches.”

“It seems,” he says, “that not solely was that the optimum [approach], however so easy that it turned helpful instantly.”

Photo of Jacob Ziv (left) and Abraham Lempel.Jacob Ziv (left) and Abraham Lempel revealed algorithms for lossless information compression in 1977 and 1978, each within the IEEE Transactions on Data Concept. The strategies turned often called LZ77 and LZ78 and are nonetheless in use at present.Picture: Jacob Ziv/Technion

Whereas Ziv and Lempel had been engaged on LZ78, they had been each on sabbatical from Technion and dealing at U.S. firms. They knew their growth could be commercially helpful, and so they needed to patent it.

“I used to be at Bell Labs,” Ziv remembers, “and so I believed the patent ought to belong to them. However they stated that it isn’t potential to get a patent until it is a piece of {hardware}, and so they weren’t fascinated about making an attempt.” (The U.S. Supreme Courtroom did not open the door to direct patent safety for software program till the Nineteen Eighties.)

Nevertheless, Lempel’s employer, Sperry Rand Corp., was keen to strive. It received across the restriction on software program patents by constructing {hardware} that applied the algorithm and patenting that machine. Sperry Rand adopted that first patent with a model tailored by researcher Terry Welch, referred to as the LZW algorithm. It was the LZW variant that unfold most generally.

Ziv regrets not having the ability to patent LZ78 instantly, however, he says, “We loved the truth that [LZW] was highly regarded. It made us well-known, and we additionally loved the analysis it led us to.”

One idea that adopted got here to be referred to as Lempel-Ziv complexity, a measure of the variety of distinctive substrings contained in a sequence of bits. The less distinctive substrings, the extra a sequence could be compressed.

This measure later got here for use to examine the safety of encryption codes; if a code is really random, it can’t be compressed. Lempel-Ziv complexity has additionally been used to research electroencephalograms—recordings {of electrical} exercise within the mind—to
decide the depth of anesthesia, to diagnose despair, and for different functions. Researchers have even utilized it to analyze pop lyrics, to find out developments in repetitiveness.

Over his profession, Ziv revealed some 100 peer-reviewed papers. Whereas the 1977 and 1978 papers are probably the most well-known, info theorists that got here after Ziv have their very own favorites.

For Shlomo Shamai, a distinguished professor at Technion, it is the 1976 paper that launched
the Wyner-Ziv algorithm, a manner of characterizing the boundaries of utilizing supplementary info accessible to the decoder however not the encoder. That drawback emerges, for instance, in video purposes that benefit from the truth that the decoder has already deciphered the earlier body and thus it may be used as facet info for encoding the following one.

For Vincent Poor, a professor {of electrical} engineering at Princeton College, it is the 1969 paper describing
the Ziv-Zakai certain, a manner of understanding whether or not or not a sign processor is getting probably the most correct info potential from a given sign.

Ziv additionally impressed quite a few main data-compression consultants by means of the lessons he taught at Technion till 1985. Weissman, a former pupil, says Ziv “is deeply passionate concerning the mathematical great thing about compression as a option to quantify info. Taking a course from him in 1999 had a giant half in setting me on the trail of my very own analysis.”

He wasn’t the one one so impressed. “I took a category on info concept from Ziv in 1979, at the start of my grasp’s research,” says Shamai. “Greater than 40 years have handed, and I nonetheless keep in mind the course. It made me keen to take a look at these issues, to do analysis, and to pursue a Ph.D.”

Lately, glaucoma has taken away most of Ziv’s imaginative and prescient. He says {that a} paper revealed in IEEE Transactions on Data Concept this January is his final. He’s 89.

“I began the paper two and a half years in the past, after I nonetheless had sufficient imaginative and prescient to make use of a pc,” he says. “On the finish, Yuval Cassuto, a youthful school member at Technion, completed the venture.” The paper discusses conditions wherein massive info recordsdata must be transmitted shortly to distant databases.

As Ziv explains it, such a necessity might come up when a health care provider needs to match a affected person’s DNA pattern to previous samples from the identical affected person, to find out if there was a mutation, or to a library of DNA, to find out if the affected person has a genetic illness. Or a researcher learning a brand new virus might need to examine its DNA sequence to a DNA database of recognized viruses.

“The issue is that the quantity of knowledge in a DNA pattern is large,” Ziv says, “an excessive amount of to be despatched by a community at present in a matter of hours and even, typically, in days. In case you are, say, making an attempt to determine viruses which can be altering in a short time in time, which may be too lengthy.”

The method he and Cassuto describe entails utilizing recognized sequences that seem generally within the database to assist compress the brand new information, with out first checking for a selected match between the brand new information and the recognized sequences.

“I actually hope that this analysis could be used sooner or later,” Ziv says. If his observe report is any indication, Cassuto-Ziv—or maybe CZ21—will add to his legacy.

This text seems within the Could 2021 print situation as “Conjurer of Compression.”

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