I NASA Technical Memorandum 102325 

Digital CODEC for Real-Time Processing 

of Broadcast Quality Video Signals 

at 1 8 Bits/Pixel 

Mary Jo Shalkhauser and Wayne A. Whyte, Jr. 

Lewis Research Center 

Cleveland, Ohio 

(NASA-Tll-102325) DIGITAL CODEC FOB lV8 9 -279 2 7 REAL-TIHE PBOCESSIIG OF BRO&DCAST QUALITY 

Lewis Research Center) 16 p CSCL 17B Dnclas 

VIDgO SIGWALS AT 1.8 BITS/PIXBL IMASaG3/32 0225953 

Prepared for the 

* Global Telecommunications Conference 

sponsored by the Institute of Electrical and Electronics Engineers 

Dallas, Texas, November 27-30, 1989 

i 
.. ,_, 

DIGITAL CODEC FOR REAL-TIME PROCESSING OF BROADCAST QUALITY VIDEO SIGNALS AT 1.8 BITSIPIXEL 

, 

Mary Jo Shalkhauser and Wayne A. Whyte. Jr. 

National Aeronaut 

Lewi s 

C1 eve 

11. Abstract 

Advances in very large-scale integration 

and recent work in the field of bandwidth 

efficient digital modulation techniques have 

combined to make digital video processing 

technically feasible and potentially cost competitive for broadcast quality television 

transmission. A hardware implementation has 

been developed for a DPCM-based digital television bandwidth compression algorithm which 

processes standard NTSC composite color 

television signals and produces broadcast 

quality video in real time at an average of 

1.8 bitslpixel. This paper describes the data 

compression algorithm and the hardware implementation of the codec, and provides performance results. 

11. Introduction 

Transmission of television signals in a digital 

format has been looked upon with promise for a 

number of years. Digital systems providing teleconferencing quality video have become common place 

in both government and industry. However, digital 

transmission of high-quality (toll grade or broadcast quality) television signals has yet to achieve 

anything close to the same kind of acceptance. 

This has been due in part to the broadcasters' 

reluctance to have processing of any kind performed 

on the transmitted signals. 

extent, digital transmission of broadcast quality 

video has failed to gain acceptance because it has 

not been cost effective to do so. The lack of 

available wideband digital links as well as the 

complexity of implementation of bandwidth efficient digital video CODEC's (encoder/decoder) has 

worked to keep the cost of digital television 

transmission too high to compete with analog 

methods. 

But to a greater 

cs and Space Administration 

Research Center 

and, Ohio 44135 

Advances in vsiry large-scale integration 

(VLSI) as well as recent work in the field of 

advanced digi tal modul$.rlon techniques have combined to make dtqqtal video processing technically 

feasible and potqntially cost competitive for 

broadcast quality television transmission. The 

coupling 'of a transparent, bandwidth efficient data 

compression technique with a bandwidth efficient 

modulation technique offer the potential for transmission of two (or more) high-quality television 

signals in the same bandwidth occupied by a single 

frequency modulated television signal. This paper 

presents the hardware implementation of a digital 

television bandwidth compression algori thm which 

processes standard NTSC (National Television Systems 

Committee) composite color television signals and 

produces broadcast quality video in real time at an 

average of 1.8 bits/pixel. 

element, represents each piece of sampled data. 

The sampling rate used with this algorithm results 

in 768 samples over the active portion of each 

video line by 512 active video lines per video 

frame.) The algorithm is based on differential 

pulse code modulation (DPCM), but additionally 

utilizes a nonadaptive predictor, nonuniform quantizer and multilevel Huffman coder to reduce the 

data rate substantially below that achievable with 

straight DPCM. 

level Huffman coder combine to set this technique 

apart from prior-art DPCM encoding algorithms. 

Section I11 below will provide the details of the 

compression algorithm while sections IV and V discuss the hardware implementation and performance 

results, respectively. 

(A pixel, or picture 

The nonadaptive predictor and multi111. Data Compression Algorithm 

Differential pulse code modulation has historically been one of the most popular predictive 

image coding methods studied, due to its simplicity 

of implementation and overall subjective performance characteristics. The fault of DPCM schemes in 

the past have been that 3 to 4 bitslpixel were 

required to achieve acceptable image quality, with 

4 bits/pixel generally preferred to maintain a 

broadcast qual i ty picture representation. 

tem presented here combines the simplicity of the 

basic DPCM approach with several performance 

enhancements to achieve broadcast quality images 

at an average 1.8 bits/pixel. 

A block diagram of the compression scheme is 

presented in Fig. 1. The DPCM portion utilizes an 

intrafield approach with a two-dimensional predictio? based on averaging neighboring pixel values 

having the same color subcarrier phase relationship 

as the current pixel . Sampling of the compos1 te 

analog video signal is done at four times the color 

subcarrier frequency rate (4 x 3.579545 MHz). Figure 2 shows the spacial relationship of the current 

pixel and the two pixels used to generate the predicted value, PV, in Fig. 1. The pixels used are 

the fourth previous pixel from the same line and 

The sys1 
DIGITIZED 

VIDEO IN 

I 

I 

I 

I 

I 

I QUANTIZER I 

VALUE I I I I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

~~~~ RP o;p 

LINE LINE 

+ DELAY DELAY +2 

NON-ADAPTIVE 

PREDICTOR 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

ENCODER I 

I 

I 

I 

I 

I 

I 

I 

I NON-ADAPTIVE 

I PREDICTOR I 

I 

I 

I 

RECONSTRUCTED I 

DlGlTlZEDVlDEO 4 1 I I OUT 

I I 

I 

I 

I 

I 

I I 

I I 

I I 

I 

I 

I 

I 

I 

I DECODER I 

SAMPLES 

r2 LINES PREVIOUS PIXEL 

CONSECUTIVE 

LINESOFA -0 0 0 0 0 0 0 0 

FIELD -o~oooqoo 

4TH CURRENT PIXEL 

PREVIOUS 

PIXEL 

Figure 2. - Anabg video signal sampling and DPCM pixel relationships. 

the same pixel from two lines previous in the same current pixel value to obtain a difference value 

field. These neighboring pixels have the same to be quantized. 

color subcarrier phasing as the current pixel and 

will therefore be highly correlated.. The two pixel Figure 1 shows a "nonadaptive predictor" 

values are averaged to produce the prediction of 

the current pixel value. At this point the algo- value along with the predicted value, PV. The 

rithm differs from standard DPCM. vlhere the pre- function of the NAP is to further improve the 

dicted value would simply be subtracted from the 

(NAP) value being subtracted from the current pixel 

prediction of the current pixel. The nonadaptive 

2 
predictor estimates the difference value obtained 

when the DPCM prediction is subtracted from the 

current pixel value (PIX - PV). The subtraction 

of the NAP value from PIX - PV causes the resulting difference (DIF) value to be close to zero. 

The smaller the DIF, the more efficiently the quantized pixel information can be rransnitted aue to 

the use of Huffman coding prior to rransmission 

over the channel. (Huffman coding assigns variable length codewords based upon probability of 

occurrence. The application of Huffman coding to 

this algorithm will be discussed later.) 

The development of the nonadaptive predictor 

was predicated on the likelihood that the difference values of adjacent pixels are similar. The 

difference between the current pixel value and its 

prediction, PV, is estimated and subtracted off by 

way of the NAP prior to quantiztion. The estimate 

is simply based on the value of DIF for the previous pixel. 

estimates are prestored and do not change with differing picture content. 

were generated from statistics of numerous television images covering a wide range of picture 

content. 

ference values calculated within the boundaries of 

the difference values for each quantization level. 

Table I shows the NAP values corresponding to each 

quantization level. 

values in Table I; if the difference value (DIF) 

for the previous pixel was 40, corresponding to 

quantization level 11, the value of NAP to be subtracted off from the current pixel difference would 

be 38. To reconstruct the pixel, the decoder uses 

a lookup table to add back in the appropriate NAP 

value based upon knowledge of the quantization 

level from the previously decoded pixel. The use 

of the NAP results in faster convergence at transition points in the image, thereby improving edge 

detection performance. The rapid convergence also 

reduces the total data requirements by increasing 

the percentage of pixels in quantization level 7, 

which is assigned the shortest codeword length by 

the Huffman coding process. 

The NAP is nonadaptive because the 

These prestored values 

The NAP values represent the average difTo give an example using the 

Difference 

(DIF) 

value 

-255 to -86 

-85 to -60 

-59 to -34 

-33 to -19 

-18 to -9 

-a to -4 

-3 to 3 

4 to 8 

9 to 18 

19 to 33 

34 to 59 

60 to 85 

86 to 255 

TABLE I. - QUANTIZATION AND NONADAPTIVE 

PREDICTION 

Quantization 

level 

(QL) 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

Quantizat i on 

value 

CQV) 

-100 

-66 

-42 

-25 

-14 

-6 

0 

6 

14 

25 

42 

66 

100 

Nonadaptive 

prediction 

(NAP) 

value 

-85 

-61 

-38 

-22 

-1 1 

-4 

0 

4 

11 

21 

38 

61 

84 

The quantizer shown in Fig. 1 has 13 levels. 

Each level has a quantization value associated 

with a range of difference values as indicated in 

Table I. The quantizer is nonuniform so that more 

levels are provided for small magnitude differences which would result from subtle changes in 

picture content. The human eye is sensitive to 

small variations in smooth regions of an image and 

can tolerate larger variations near transition 

boundaries where large difference values are 

more likely to occur. The nonadaptive predictor 

discussed previously, acts to reduce the difference 

values thus improving image quality by reducing the 

quantization error. This is because the nonuniform 

quantizer results in lower quantization error for 

small magnitude differences than for large magnitude 

differences. The number of quantization levels, 

the corresponding difference value ranges, and the 

specific quantization values shown in Table I were 

experimentally derived through subjective evaluation of sample images processed by computer simulation of the encoding algorithm. 

The final major aspect of the encoding algorithm is the multilevel Huffman coding process. 

Huffman coding of the quantized data allows shorter 

codewords to be assigned to quantized pixels having 

the highest probability of occurrence. A separate 

set of Huffman codes has been generated for each of 

the 13 quantization levels. The matrix of code 

sets is used to reduce the number of data bits 

required to transmit a given pixel. The particular 

Huffman code set used for a given quantized pixel 

is determined by the quantization level of the previous pixel (i.e.. if the difference value for the 

previous pixel resulted in quantization level 4 

being selected for that pixel, then the Huffman 

code set selected for the current pixel would be 

code set 4, corresponding to the probability of 

occurrence of pixels falllng into the fourth quantization level). 

As with the NAP, the Huffman code trees were 

generated by compiling statistical data from numerous images covering a broad range of picture content. 

for each of the 13 quantization levels as a function of the quantization level of the previous 

pixel. A separate Huffman code set was then generated based on the probabil i ty data of "current" 

pixels falling into each of the 13 quantization 

levels of the "previous" pixels. There is a tendency for neighboring pixels to fall into the same 

or close to the same quantization level. By recognizing and taking advantage of this fact, the use 

of the multilevel Huffman code sets provides significant reductions in bits per pixel over a 

single Huffman code tree because they allow nearly 

all pixels to be represented by short length 

codewords. 

Probability of occurrence data was compiled 

IV. Hardware Implementation 

The configuration of the data compression 

hardware is shown in Fig. 1. The NTSC format video 

signal is digitized by sampling with an analog-todigital (A/D) converter at a rate of four times the 

color subcarrier frequency (approximately 14.32 MHz). 

The AID converter has an 8-bit resolution allowing 

3 
each sample, called a pixel, to be represent 

one of 256 digital levels. The 8-bit pixels 

input to the enccder at the 14.32 MHz rate. 

encoder Compresses the video data and serial 

transmits a comoressed representation of the 

over a channel at a rate of approximately 

25 Mbps :megabits/sec) to the decoder. 

The decoder receives the compressed ser 

d by 

are 

The 

Y 

data 

a1 

data ana reconstructs a facsimile of the original 

viaeo data. The reconstructed &bit pixels are 

converted back to an analog video signal using a 

digital-to-analog (D/A) converter. For this implementation, the entire video signal including the 

horizontal and vertical synchronization pulses, 

the color burst, and the active video is sampled 

and compressed. In future implementations, all 

but the active video part of the signal will be 

removea by the encoder and the decoder will reconstruct these portions and reinsert them into the 

signal, thereby increasing the overall compression 

of the picture. 

For this breadboard version of the compression 

hardware, TTL (transistor-transistor logic) was 

chosen as the implementation technology due to ease 

of usage and wide availability of devices. The 

hardware designs were constructed on wire-wrap 

boards and mounted in a 5-slot 19-in. chassis. 

En coder 

The encoder portion of the compression hardware digitizes the analog video signal into 8-bit 

pixels, compresses the image, and converts the 

resultant data to a serial bit stream. A detailed 

description of each of the encoder blocks (shown 

in Fig. 1) follows. 

is shown in Fig. 3. This DPCM circuit averages 

previous neighboring pixel values to predict the 

current pixel value. The previous pixels of the 

same color subcarrier phase as the current pixel 

are obtained using a 4-pixel delay and a 2-line 

delay. The 4-pixel delay is implemented using four 

8-bit registers in a shift register .configuration. 

The 2-line delay is implemented using a random 

access memory (RAM) which is addressed by a counter 

that recycles every two lines. For the first two 

The differential pulse code modulation circuit 

lines of each field, the RAM is loaded with the 

reconstructed values of the original pixels, while 

the output register of the 2-line delay is zeroed. 

For every line thereafter, the pixel value of two 

lines previous is read out of the RAM and then the 

new reconstructed pixel (RP) value is written into 

the same memory location. Then the address counter 

is incremented to the next memory location for the 

next pixel prediction. 

delay are added together using two cascaded 4-bit 

full adders. The divide-by-two function is performed by dropping the adder's least significant 

output bit and using the carry-out signal as the 

most significant bit. This is the same as a "shift 

right with carry" of the adder outputs. During the 

first two lines of each field, the DPCM prediction 

circuit uses only the 4th previous pixel on the 

current line to predict the current pixel value. 

In this case, the 2-line delay input to the adder 

is zeroed and the divide-by-two circuit is 

by-passed using a multiplexer. 

tive prediction (NAP) value are subtracted (subtractions are performed as two's complement 

additions) from the original pixel value resulting 

in a difference value (DIF = PIX - PV - NAP). 

These difference values are then grouped into quantization levels (QL), created from a look-up table 

Implemented In programmable read-only memory (PROM) 

using the difference (DIF) value as the address. 

The quantization levels are delayed by one pixeltime and used to address another PROM look-up table 

to create the nonadaptive prediction (NAP). The 

nonadaptive predictor estimates the current DPCM 

difference value (PIX - PV) from the difference 

value of the immediately previous pixel. 

The quantization value CQV), an estimation of 

the DIF, is created from another PROM look-up 

table. In this case, the quantization level is 

used to address the memory locations which contain 

the quantization values. I 

The two-dimensional Huffman codes are created 

by yet another PROM look-up table. 

quantization level (QL) and the immediately previous quantization level (QLn-l) address a PROM which 

contains, at each location, a 1 to 12-bit Huffman 

The outputs of the 2-line delay and the 4-pixel 

The DPCM predicted value (PV) and the nonadapThe current 

DIVIM-BY 2 

ADDER MUX -pv NO DNIDE-BY 2 

RP RECONSTRUCTED 

PIXEL 

PIX PIXEL 

PV PREDICTED 

VALUE 

Figure 3. - Differential pulse code modulation circuit. 

4 
code and a 4-bit code which specify the length of 

the Huffman code. 

The outputs of the Huffman encoder are multiplexed with the first four pixels of every line so 

that the DPCM circuit has a valid starting point. 

The output of this multiplexer is input into a bank 

of first-in, first-out (FIFO) memories. Forty FIFO 

integrated circuits are configured with expanded 

width and depth to achieve a Sank of FIFO memory 

18 bits wide and 72 K deep. The FIFOs are necessary to compensate for the variable lengths of the 

Huffman codes and the differences between the FIFO 

input frequency and the FIFO output frequency. On 

the input-side of the FIFO's, the data is written 

periodically at the pixel rate of 14.32 MHz. On 

the output side of the FIFO's, data is read out at 

a variable rate depending on the length of the 

Huffman codes and the frequency of the serial data. 

Sixteen of the FIFO's bits are data (either 

actual pixel values for the first four pixels of 

each line or Huffman codes) and length of data. 

The other two bits are used to pass line and field 

flags, indicating the start of each line and each 

field. The line and field flags are used for 

insertion of unique words into the data. 

fie d and line timing at the decoder. Due the variab e length nature of the Huffman codes, channel 

bit errors can often result in improper detection 

of he codes by the decoder. Unique words allow 

the line and field timing to get back on track in 

the event of bit errors, to minimize the impact to 

the quality of the reconstructed video images. 

Different unique word values are used for lines 

and fields so they can be detected separately. In 

both cases, unique words were chosen to avoid duplication by valid Huffman codes. 

unique words are currently used; however, the hardware was flexibly designed so that the unique word 

content and length can be changed If necessary. 

are monitored to allow insertion of the unique 

words at the proper position within the data. When 

a line or field flag is detected, FIFO reads are 

stopped to allow time for the unique words to be 

multiplexed within the data (see Fig. 4). Like the 

Huffman codes, the unique words must contain a 

4-bit code indicating the length of the unique 

words. The unique words are divided into two 8-bit 

sections each accompanied by a length code. 

insertion of the unique word, the FIFO reads are 

reactivated. 

allel format to a serial format for transmission 

over a channel. Because lengths of the Huffman 

codes vary, a variable length parallel-to-serial 

converter must be used. The parallel-to-serial 

converter (shown in Fig. 4) consists of a 12-bit 

parallel load shift register and a counter. The 

Huffman codes are loaded into the shift register 

and the 4-bit length of the Huffman code is loaded 

into the counter. The counter counts down as the 

shift register shifts out the data into a serial 

bit stream. When the counter reaches zero the 

Unique words are necessary to maintain proper 

Sixteen-bit 

The line and field flags at the FIFO outputs 

After 

Next, the data must be converted from the parshifts stop and a new code is read from the FIFO 

memory. Then the shift register and counter are 

loaded with new values and the shifting process 

reDeats. 

Decoder 

The decoder circuit receives the serial data 

that the encoder transmitted and reconstructs a 

representation of the original 8-bit pixels, and 

using a dlgital-to-analog (DIA) converter, creates 

an analog video signal. A detailed description of 

each of the decoder blocks (Fig. 1) follows. 

lel circuits: line unique word detect, field 

unique word detect, and Huffman decoder. The 

unique word detect circuits allow detection of 

unique words with bit errors by selection of an 

error threshold of up to 3-bit errors. A block 

diagram of the unique word detect circuit is contained in Fig. 5. The serial data is first shifted 

into a 16-bit shift register. The 16-bit parallel 

output of the shift register is exclusive-or'd 

(XOR) with the correct unique word value set in dip 

switches. Next, the number of ones contained in 

the XOR outputs are summed using adders. A circuit 

at the output of the adders allows selection of the 

error threshold and creates a pulse if a unique 

word within that error threshold is detected. The 

unique word detect pulse is ANDed with a unique 

word window signal which disallows unique word 

detects until close to the expected location of 

valid unique words. The windowing technique lowers 

the probabi 1 i ty of false detects. 

The Huffman decoder receives the data in a 

serial format from the output of a 16-bit shift 

register that parallels the unique word detect circuits. When unique words are detected, the Huffman 

decoder is disabled while the unique word is purged 

from the shift register to avoid Huffman decoding 

of unique words. 

as a tree search in memory. The address to the 

Huffman decoder PROM is initially set to zero (top 

node of the Huffman code tree). The content of 

each memory location consists of the next two possible addresses to the memory denoting the next 

two tree branches. As each serial bit is received, 

it is used by a multiplexer to select the next memory address. A serial "one" selects one address 

(branch) and a serial "zero" selects the other 

address (branch). The new address (new tree node) 

also contains the next two possible tree branches 

based upon the next received serial bit. The tree 

search continues in this manner until the least 

significant output bit of the memory is high, indicating the end of a valid Huffman code. At this 

point, the other memory output bits contain the 

correct quantization value (QV) and quantization 

level (QL) for the received Huffman code. The 

PROM address is then reset to zero (the top node 

of the tree) and the decoding process continues. 

As the Huffman codes are detected, the resultant quantization levels and values are written into 

FIFO's. These FIFO's, like in the encoder, are 

The input to the decoder contains three paralThe Huffman decoder (Fig. 6) is implemented 

5 
FIFO 'OUTPUT - 

FIELD 4 

UNIQUE 

7 

COUNTER 

MUX - 4- WORD LENGTH 

MUX 

SERIAL 

CLOCK 

WORD LINE ----C 

16-BIT SHIFT UNIQUE WORD 

DIP SWITCHES DATA 

DATA WORD 

ADDERS 

DIP 

THRESHOLD LOGIC 

UNIQUE 

WINDOW 2 

Figure 5. - Unique word detect circuit. 

SWITCH 

A ROM D 

A NON-ADAPTIVE T - PREDICTION A MUX + LATCH 

0 

INITIAL ----C OLn-' w 

VALUE A 

- WORD 

SERIAL 

DATA 

ZERO 

DATA 

1 

t NEXT 

ADDRESS - QL 

A 

D APROM SEL D 

*D 1 'cA MUX 

- A A AB 

MUX 

-B 

t 

LOOK-UP m QUANTIZATION 

e VALUE 

TABLE 

1 NEXT 

ADDRESS A 

D APROM SEL D 

*D 1 'cA MUX 

A A AB 

A 

-B 

MUX 

t 

LOOK-UP m QUANTIZATION 

e VALUE 

TABLE 

FLAG 

Figure 6. - Huffman decoder circuit. 

required to absorb the differences in the variable 

length Huffman codes and the pixel rate at the output of the decoder circuit. In conjunction with 

the unique word detects and the line and field 

counters, the FIFO writes and reads are controlled 

to compensate for synchronization problems created 

by improper Huffman decoding due to bit errors. 

The FIFO outputs, quantization levels and 

quantization values, are used to reconstruct the 

original video image. The quantization level is 

delayed by one pixel-time and used by a PROM 

look-up table to create the nonadaptive prediction 

(NAP). The quantization value CQV) is added to the 

nonadaptive prediction (NAP) value and the OPCM 

prediction value (PV) to create the reconstructed 

pixel values (RP). The decoder DPCM circuit implementation is identical to the encoder DPCM circuit. 

The RP values are input to a D/A converter which 

converts the reconstruct pixel values to an analog 

video signal. 

G 
ONGINAL PAGE 

j3lJ$W AUQ WHITE PHOTOGRAPH 

(a) Original image (8 bpp). (b) Processed image (1.949 bpp) 

IC) Onginal inage (8 bw) Id) Processed image (1.815 bpp! 

(e) Original image (8 bpp) (f)Processedimage(l.711 bpp) 

(9) Onginal image (8 bpp). (h) Processed image(l.671 bpp) 

TABLE 11. - ENCODING 

ALGORITHM PERFORMANCE 

RESULTS 

Scene 

Beach 

Make up 

Lawyer 

Star Trek 

Bees and flowers 

Game/girl 

Woman/couch 

Hospital 

Woman /man 

Woman/gray sui t 

Two men 

Manlphone 

Fuller brush 

P 1 ane / b i p 1 ane 

Womanlhand 

News woman 

Color bars 

Two women 

Average 

Bi ts/ 

pixel 

2.228 

1.738 

1.976 

1.682 

1.996 

1.689 

1.813 

1.979 

1.802 

1.872 

1.949 

1.815 

2.031 

1.711 

1.671 

1.823 

1.347 

1.659 

1.822 

Figure 7. - Results 01 mmputer sirnulalion processing of NTSC video inages (bpp -bits per pixel). 

7 
V. Performance Results 

Table I1 provides results of computer simulation of the encoding algorithm described in this 

paper. The picture content across the images is 

representative of the broad range of material which 

makes up typical television viewing. The results 

show the variability of compression with ComDlexity 

of oicture content. Standard color bars, containing considerable redundancy, can be processed very 

efficiently at 1.347 bitslpixel. The Beach scene, 

one of the SMPTE color reference subjective testing 

slides, requires 2.228 bits/pixel; an indication 

of the complex nature of the scene. 

images fall somewhere between these bounds, with 

an average across all scenes of 1.822 bitslpixel. 

Figure 7 shows original and reconstructed images. 

The reconstructed image quality in all cases 

is excellent - reconstructed images are indlstlnguishable from the 8 bits/pixel digitized originals. A two channel video frame storage unit was 

used in the comparison of original versus processed images. This provided a means for very critfhe other 

ical comparative testing, since side-by-side as 

well as switched comparisons were possible. Interframe motion is not degraded by the processing 

since the algorithm is an intrafield coding process. Motion sequences of "real-time" video were 

processed on a frame-by-frame basis and pieced 

together using a type "C" 1-in. broadcast video 

tape recorder with animation editing capabilities. 

No motion artifacts were present in the reconstructed sequence as expected. 

pression algorithm is relatively straight forward 

and can be easily implemented in VLSI for performance improvement, size reduction and cost effectiveness. Combining the data compression hardware 

with a bandwidth efficient modulation technique 

such as 8-PSK (practical bandwidth efficiency of 

greater than 2 bits/sec/Hz in a power limited system) will enable two or more broadcast quality 

television signals to be transmitted through a single C-band transponder, creating a substantial 

bandwidth improvement over analog television 

transmissions. 

The hardware design of this video data com8 
cw\s/\ National Aeronautics and Report Documentation Page 

1. Report No. 

NASA TM-102325 

2. Government Accession No. 

7. Author@) 

Mary Jo Shalkhauser and Wayne A. Whyte, Jr. 

17. Key Words (Suggested by Author(@) 

Data compression; Video; DPCM; Huffman coding; 

Digital television 

9. Performing Organization Name and Address 

National Aeronautics and Space Administration 

Lewis Research Center 

Cleveland, Ohio 44135-3191 

2. Sponsoring Agency Name and Address 

National Aeronautics and Space Administration 

Washington, D.C. 20546-0001 

18. Distribution Statement 

Unclassified -Unlimited 

Subject Category 32 

5. Supplementary Notes 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 

Unclassified Unclassified 

3. Recipient's Catalog NO. 

21. No of pages 22. Price' 

10 A02 

5. Report Date 

6. Performing Organization Code 

8. Performing Organization Report No. 

E-5026 

IO. Work Unit No. 

643- 10-01 

11. Contract or Grant No. 

13. Type of Report and Period Covered 

Technical Memorandum 

14. Sponsoring Agency Code 

Prepared for the Global Telecommunications Conference sponsored by the Institute of Electrical and Electronics 

Engineers, Dallas, Texas, November 27-30, 1989. 

6. Abstract 

Advances in very large-scale integration and recent work in the field of bandwidth efficient digital modulation 

techniques have combined to make digital video processing technically feasible and potentially cost competitive for 

broadcast quality television transmission. A hardware implementation has been developed for a DPCM-based 

digital television bandwidth compression algorithm which processes standard NTSC composite color television 

signals and produces broadcast quality video in real time at an average of 1.8 bitdpixel. This paper describes the 

data compression algorithm and the hardware implementation of the CODEC, and provides performance results. 

I 'For sale by the National Technical Information Service, Springfield, Virginia 22161 NASA FORM 1826 OCT 86