Reflective binary encoder for vector quantization

A binary encoder for vector quantization is provided which comprises a plurality of identical two-level branch selectors connected in a turnaround cascade pipeline array. The upper levels of the two-level selectors are connected in series and the first selector receives a formatted digital data vector input. The upper level of last selector has its output connected to its own lower level input and the outputs of the lower level selectors are connected in series so that the last lower level selector in the turnaround cascade resides in the first two level selector. The output of the last lower level selector provides a desired compressed data vector output.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a new reflective binary encoder for vector 
quantization. More particularly, the present invention relates to a vector 
quantization encoder and to a novel code book tree for use with vector 
quantization encoders to provide a non-uniform binary tree code book. 
2. Description of the Prior Art 
Vector quantizers are known and are described generally in the 
publications: "An Algorithm for Vector Quantizer Design" by Y. Linde, A. 
Buzo and R. Gray in IEEE Trans. on communications, Vol. Com-28, No. 1, 
Jan. 1980 and in "Vector Quantization in Speech Coding" by J. Makhoul, S. 
Roucos and H. Gish in Proceedings of the IEEE, Vol. 73, No. 11, Nov. 1985. 
In my U.S. Pat. No. 4,727,354, there is disclosed a full search encoder for 
searching a random memory array but does not teach or suggest a reflective 
binary encoder or a non-uniform binary code tree book. 
The article by Linde, Buzo and Gray, mentioned above, discloses an 
algorithm for designing a full search code book which has no code book 
tree structure to enhance search speed. This algorithm is widely known as 
the LBG algorithm. 
The article by Makhoul, et al, mentioned above, discloses an algorithm for 
searching code books with embedded tree structures. A uniform and a 
non-uniform binary tree structure is disclosed. 
It would be desirable to provide a new and improved encoder for vector 
quantization and associated therewith an improved non-uniform binary tree 
structure which enables faster encoding than the above-mentioned prior art 
structures. 
SUMMARY OF THE INVENTION 
It is a principal object of the present invention to provide a novel method 
for providing a new and improved non-uniform binary tree code book. 
It is a primary object of the present invention to provide a novel code 
book structure which is an improvement over the prior art code book tree 
structures. 
It is another principal object of the present invention to provide a 
reflective binary encoder having a new architecture. 
It is a principal object of the present invention to provide a novel 
reflective binary encoder which employs a novel non-uniform binary tree 
code book. 
It is a primary object of the present invention to provide a novel 
reflective binary encoder architecture which optimizes the use of commonly 
available logic and memory integrated circuit chip devices. 
It is another principal object of the present invention to provide a novel 
reflective binary encoder architecture which is proven to provide a higher 
throughput rate than prior art structures. 
It is another principal object of the present invention to provide a novel 
reflective binary encoder architecture constructed as an expandable 
cascade of identical chip devices. 
It is a general object of the present invention to provide a novel two 
level branch selector which assists distribution of code book vectors so 
they may be searched in a cascaded pipeline structure. 
It is another general object of the present invention to provide a novel 
selector structure which provides a best fit between computation 
complexity and memory access. 
It is another general object of the present invention to provide a novel 
branch selector which employs an internal memory and an expandable 
external memory. 
It is another general object of the present invention to provide a novel 
reflective binary encoder architecture which may be implemented by 
decreasing the number of branch selector chips to increase the data 
compression ratio. 
According to these and other objects of the present invention, there is 
provided a new and improved encoder for vector quantization and associated 
improved non-uniform binary tree structure. The reflective binary encoder 
comprises a plurality of two level branch selectors arranged in a cascade 
or string, each of which computes two levels of a binary tree. The 
pipeline path through the cascade arranged two level branch selectors is 
arranged such that the sum of the two levels of the branch selector always 
equals the number of levels in the code book tree.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
Refer now to FIG. 1 showing a schematic block diagram of an encoding system 
10 having a digital pixel information source 11. Information describing 
each of the pixels in the array 11 are supplied on line 12 to a vector 
formatter 13. Depending on the type of digital information to be 
formatted, such as pixels from an imaging source, digitized speech 
information, synthetic aperture radar information, the sample vector 
source in digital form may be formatted by a vector formatter 13. Vector 
information on line 14 is applied to the novel reflective binary encoder 
15, which will be explained in greater detail hereinafter. While shown as 
a separate element, the code book tree 16 is functionally a part of the 
reflective binary encoder 15. In the preferred embodiment of the present 
invention, the non-uniform binary code book tree is pre-computed by a 
general purpose processors which examines a plurality of typical vectors 
from the information source and computes candidate vectors for minimum 
distortion, preferably employing the LBG algorithm mentioned hereinbefore. 
In the present embodiment invention, reflected binary encoder, a code book 
tree 16 will always be generated in order to compress the desired digital 
data to produce the compressed data on output line 17. 
Refer now to FIG. 2 showing a simplified block diagram of a novel 
reflective binary encoder architecture. The encoder 15 is shown comprising 
the input formatter 13 which produces the vectors on line 14 to the input 
of the reflective binary encoder 15 which comprises a plurality of 
two-level branch selectors 18-21. The input vector on line 14 enters the 
upper level of the two-level branch selector 18 and continues along the 
pipeline indicated by lines 22-25 and is reversed and returned through the 
lower level of the branch selectors 21 to 18 via a return pipeline 
indicated by lines 26-29. The line 29 for this illustration is the same as 
lines 17 of FIG. 1 on which the compressed data is provided. The levels of 
the individual selectors 18-21 have been indicated to be TL 0, TL 1, TL 2, 
and tree level TL N/2-1. In the return pipe line path, the lower branch of 
the selectors are shown numbered as TL N/2, TL N-3, TL N-2 and TL N-1 for 
the fourth and the last tree level of a tree having N levels. 
Refer now to FIG. 3 showing a detailed block diagram of one of the novel 
modular two-level branch selectors 18-21. The vector on line 14 is shown 
entering a vector buffer 31 which holds the vector until the computation 
is complete and produces a vector output on line 22. The address pointer 
on tree pointer line 14' is initialized to zero by a controller or 
external processor (not shown). The tree pointer TP is applied to the TP 
conversion circuit 32 along with vector digital bit information on line 33 
from the first vector comparator 34 to produce a new pointer for the next 
selector on line 22' shown as TP+1. The tree pointer on line 14' is 
applied to the internal memory 35 as an address to produce two vectors 
C.sub.1 and C.sub.2 on output line 36 which are applied to the first 
vector comparator 34 along with the vector on line 14. The vector C.sub.1 
and C.sub.2 are compared with vector on line 14 to determine the single 
vector having the minimum distortion on line 33 to produce the vector 
digital bit information results on line 33 as mentioned before. As will be 
explained hereinafter, the single bit information on line 33 identifies 
the vector C.sub.1 or C.sub.2 having the minimum distortion from the input 
vector on line 14 which will enable the next stage selector to continue 
the search along the proper branch. 
Branch selector 18 is shown having a return pipeline path which includes 
vector information on line 28 which is applied to vector buffer 37 during 
the minimum distortion calculation which occurs in the lower level of the 
two-level branch selector 18. The pointer for the external memory 38 is 
shown at line 28' as designated TP+(N-1) and is also applied to the tree 
pointer conversion circuit 39 to produce the new pointer output TP+N on 
line 29'. The purpose of the tree pointer conversion circuit 39 is to 
provide a mapping of the non-uniform binary tree structure to a linear 
array as required by memory 38. External memory 38 produces two new 
vectors C.sub.1 and C.sub.2 for the level of memory being searched which 
are coupled via line 41 to the second vector comparator 42 shown having a 
second vector input from vector input line 28. Vector comparator 42 
determines the vector C.sub.1 or C.sub.2 having the minimum distortion 
from the vector on line 28 and produces an information bit on line 43 
which is applied to the tree pointer conversion circuit 39 so as to 
identify the pointer TP+N which identifies the desired compressed data. 
When the branch selector is not the first branch selector of the chain, 
the output vector on line 29 is passed on to the next branch selector in 
the pipeline sequence and the pointer on line 29 is coupled to another 
tree pointer conversion circuit and does not produce the final desired 
compressed data pointer TP+N, on line 29' as shown with selector 18. 
For purposes of explaining how data compression is effected by a plurality 
of identical selectors 18, assume that each pixel shown in source 11 is 
defined by 8 bits. Assume further that a cluster of 16 pixels in a 
4.times.4 mini array define one input vector to be encoded. As each 
comparison is made in the codebook tree, one bit is added to the pointer 
which identifies or points at vectors at a node in the codebook tree 16. A 
tree pointer with one bit will define level 0, i.e. node 62 or 63 
representative of a vector to be encoded as shown in FIG. 5. At level 1, 
there are four nodes or vectors which require address pointers. At level 
2, eight address pointers are required and at level N-1, 2.sub.N tree 
pointers are required. 
To accommodate this need, the bits in the pointer are shifted right one bit 
as the pointer is generated at each successive level effectively creating 
a binary word where each bit order of the word defines the branch path to 
the next lower level of the codebook tree. Thus, twenty levels may be 
defined by twenty bits and the vector of 16.times.8 bits is then 
identified by a twenty bit tree pointer. Fewer levels of compression 
require fewer tree pointer bits and the trade-off for greater data 
compression requires fewer levels in the code book tree at the expense of 
greater distortion. 
Refer now to FIG. 4 showing a more detailed block diagram of a preferred 
embodiment vector comparator 34 of the type shown in FIG. 3. The 
comparator 34 is a preferred embodiment comparator which computes the mean 
square error between the input vector on line 14 and the code book vectors 
C.sub.1 and C.sub.2 on line 36 and 36'. For purposes of simplifying the 
illustration, the source of the C.sub.1 and C.sub.2 vectors are shown in 
blocks 35 and the source of vectors from vector formatter 13 are shown in 
block 13. Components of the vectors S and C.sub.2 on lines 14 and 36 are 
applied to a differential summing circuit 44 to provide an error signal or 
value on line 45 which is squared in square law circuitry 46 to produce a 
squared error signal on line 47 to accumulator 48 to provide an 
accumulated value on line 49. In similar manner, components of the vectors 
S and C.sub.1 on lines 14 and 36' are applied to a differential summing 
circuit 51 to produce an error signal or value output on line 52 which is 
applied to the square law circuitry 53. The output of square law circuit 
53 on line 54 is applied to a second accumulator 55 to produce a second 
accumulated error signal on line 56. The accumulated error signals on 
lines 49 and 56 are applied to a differential summing circuit 57 to 
produce the desired vector bit identification information on line 33, as 
explained previously with reference to FIG. 3. Again, it will be noted, 
that the information on line 33 will be used to identify one of the limbs 
of a branch employed in the searching process when moving from one search 
level to another in the code book tree. 
Refer now to FIG. 5 showing a binary search tree 50. The root or origin of 
the tree is shown at node 58 and is identified as the root level of the 
tree and no comparison is made at the root level. Limbs 59 and 61 of the 
first branch are shown connected to nodes 62 and 63. The initial tree 
pointer is pointing at a pair of nodes and for purposes of explanation 
assume that at level zero, the pointer on line 14' is pointing at a pair 
of nodes corresponding to vectors C.sub.1 and C.sub.2 in memory 
represented by the vectors on line 36, now shown as vectors 62 and 63. Now 
for purposes of explanation, assume that the vector C.sub.1 represented by 
node 62 has the least minimum distortion and the tree pointer conversion 
circuit 32 now modifies the pointer to point at the nodes 64 and 65. Now, 
if node 64 has the least minimum distortion, the tree pointer conversion 
circuit in the next selector will point at one of the nodes 66 or 67 and 
the process continues down through the levels until it reaches the last 
level in the code book tree. Assume for purposes of illustration, that the 
node 68 is the node having the least minimum distortion after proceeding 
through the levels down to level N-1. Then, the search will stop at one of 
the nodes 69 or 70 which is known as a leaf because it has no output limb 
structure. However, it is possible that the search could have proceeded 
through node 71 to one of the leaves 72 or 73 at the level N-2 and the 
search is redundantly repeated at level N-2 where the leaf 72 or 73 is 
reselected as the vector of minimum distortion which will appear at the 
output of the data compressor on line 29 of FIG. 2 and on line 17 of FIG. 
1. 
A feature of the present invention permits the novel two level branch 
selectors to each be conducting searches at different levels in the same 
code book tree as long the search never overlaps the same level. For 
example, assume that the aforementioned search is at the level 1 where a 
comparison is being made between nodes 64 and 65. It is possible that a 
different vector search in the code book can be operational and active at 
level 2 between the nodes 74 and 75. It will be noted that the levels of 
search shown as levels 0 through level N-1 on FIGS. 2 and 5 are numbered 
the same so that the lower level search at level N-1 shown on FIG. 5 is 
being conducted in the selector 18 of FIG. 2. Similarly, if the code book 
tree is provided with 18 levels, zero to seventeen, the search at the 8th 
and 9th levels (total 17) is being conducted at the last selector 21. If 
the code book tree is provided with 24 levels, 0 to 23, the search at the 
11th and 12th (total 23) level is being conducted at the selector 21. 
Thus, it will be understood that if the levels had started with the level 
1 instead of level 0, they will end with the level N and the sum of the 
levels at each of the branches will equal the value N+1. The only reason 
for using a zero level is to better illustrate that the root level has no 
level of its own for purposes of searching the code book tree. 
A feature of the present invention is to provide a method of pre-computing 
the non-uniform code book tree by selecting the node vectors for each of 
the limbs of the branches by looking ahead at the next tree level so that 
the pair of next level node vectors have minimum distortion relative to 
the over-all training sequence distortion. It will now be explained how a 
preferred embodiment method of building a non-uniform code book tree which 
compresses with the best possible selection of data compression vectors. 
First, it is necessary to provide a large number of vectors representative 
of the digital information source in order to build the code book tree. 
Out of the total vectors in the training sequence, the two vectors having 
the least minimum distortion are selected using the conventional LBG 
algorithm. In the tree shown in FIG. 5, this is represented by the vector 
C.sub.1 and C.sub.2 designated 62 and 63. Associated with vector 62 is a 
cluster of vectors from the training sequence that are closer to the 
vector 62 than to the vector 63 and a cluster of vectors are associated 
with vector 63 having the same associated qualities with vector 63. At 
this point, in building the novel code book tree, two pseudo branches 62A 
and 62B are generated for the node 62. We also produce similar pseudo 
branches 63A and 63B for node 63. We now select the pseudo branches 62A, 
62B associated with node 62 or the pseudo branches 63A, 63B associated 
with the nodes 63 by determining which of the vectors at nodes 64, 65, 76 
or 77 produces the least minimum distortion. The one branch 62A, 62B or 
63A, 63B which has the least minimum distortion path is determined and 
both pseudo branches are added to the code book tree. This process is 
repeated at each subsequent growth of the tree. Thus, it will be 
understood that the nodes 64 and 65 have the least minimum distortion 
associated with the node 62. When the tree grows in the direction shown in 
FIG. 5, then the pseudo branches 64A, 64B and 65A, 65B are created when 
vectors 64 and 65 are added to the tree. At each growth of the code book 
tree, two pseudo nodes are made permanent nodes and four new pseudo nodes 
are created. 
The total accumulated distortion is defined by the total distortion of the 
clusters at the leaves of the tree grown to the last point of growth of 
the code book tree. By looking at all of the pseudo nodes or leaves at 
each operation of building the code book tree, a determination can be made 
which of the pseudo nodes when added to the total code book tree minimize 
the total cumulative distortion represented by the leaves of the tree. It 
will be understood that the pseudo nodes or leaves which are not yet made 
permanent nodes, are the nodes employed to determine the total cumulative 
distortion. Thus, the total cumulative distortion is being changed at each 
growth of a branch of the code book tree. This proceeds continues until 
the implementer of the encoder achieves a desired trade-off between the 
compressed data rate and distortion. Further, it is also possible to 
impose a limitation on the code book tree not to grow beyond a 
predetermined number of levels. At the conclusion of the tree groWth 
process, all of the then unused pseudo node leaves or pseudo nodes are 
discarded leaving the desired non-uniform code book tree of a desired 
number of levels. 
Having explained a preferred embodiment of the present invention reflective 
binary encoder in conjunction with a novel, non-uniform code book tree, it 
will be appreciated that the present invention data compression encoder 
permits the search of the non-uniform code book tree at very high speeds 
utilizing identical two-level branch selectors which permits accessing all 
levels of the code book tree simultaneously. Further, by providing a novel 
two-level branch selector, the entire encoder may be made from a plurality 
of identical two-level branch selectors. The novel two-level branch 
selector may be provided with on-chip memory or if additional memory is 
desired, the lower level selector of the two-level selector may utilize an 
external memory of commercially available chips of the size necessary to 
accommodate the size of the code book and the levels of search desired.